Current and future CML-rainfall estimation in Germany: Improved data processing, real-time rainfall maps and fusion with weather radar data

Many cell phone base stations are connected by a network of commercial microwave links (CMLs). At the typically used frequencies between 15 GHz and 40 GHz, precipitation along the path of a CML leads to significant attenuation of the signal. The path-averaged rain rate along a CML can therefore be derived from measurements of the attenuation.In cooperation with Ericsson, we record attenuation data of 4000 CMLs across Germany with our own open source data acquisition software. The data is acquired every minute and is available to us in real time. The dataset is continuously growing and now spans more than two and a half years.&#160;Here we present and discuss results from our current processing chain for hourly country-wide CML-derived rainfall fields. We show the effect of improved rain event detection in the raw attenuation time series and the necessity to correct for wet antenna attenuation (Graf et al., 2019). Validation is done via the gauge-adjusted radar product RADOLAN-RW of the German meteorological service. For summer months the pearson correlation between CML and radar data reaches up to 0.85, but is substantially worse during the winter months. The presented processing chain is fast enough to be applied in real-time, which will be illustrated in a live-demo. Furthermore, since Germany has both, a large network of CMLs and a modern weather radar network, we also work on the combination of these data sources. We will present first results of an approach where CMLs are used as an additional source for weather radar rain rate adjustment similarly to the existing gauge-adjustment done in RADOLAN.Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: Optimized processing and validation for one year of data, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-423, 2019

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A grid-based distributed flood forecasting model for use with weather radar data: Part 1. Formulation

Hydrology and Earth System Sciences ◽

10.5194/hess-2-265-1998 ◽

1998 ◽

Vol 2 (2/3) ◽

pp. 265-281 ◽

Cited By ~ 35

Author(s):

V. A. Bell ◽

R. J. Moore

Keyword(s):

Real Time ◽

Weather Radar ◽

Radar Data ◽

Flood Forecasting ◽

Absorption Capacity ◽

Model Parameters ◽

Grid Model ◽

Basic Model ◽

Digital Terrain ◽

Runoff Production

Abstract. A practical methodology for distributed rainfall-runoff modelling using grid square weather radar data is developed for use in real-time flood forecasting. The model, called the Grid Model, is configured so as to share the same grid as used by the weather radar, thereby exploiting the distributed rainfall estimates to the full. Each grid square in the catchment is conceptualised as a storage which receives water as precipitation and generates water by overflow and drainage. This water is routed across the catchment using isochrone pathways. These are derived from a digital terrain model assuming two fixed velocities of travel for land and river pathways which are regarded as model parameters to be optimised. Translation of water between isochrones is achieved using a discrete kinematic routing procedure, parameterised through a single dimensionless wave speed parameter, which advects the water and incorporates diffusion effects through the discrete space-time formulation. The basic model routes overflow and drainage separately through a parallel system of kinematic routing reaches, characterised by different wave speeds but using the same isochrone-based space discretisation; these represent fast and slow pathways to the basin outlet, respectively. A variant allows the slow pathway to have separate isochrones calculated using Darcy velocities controlled by the hydraulic gradient as estimated by the local gradient of the terrain. Runoff production within a grid square is controlled by its absorption capacity which is parameterised through a simple linkage function to the mean gradient in the square, as calculated from digital terrain data. This allows absorption capacity to be specified differently for every grid square in the catchment through the use of only two regional parameters and a DTM measurement of mean gradient for each square. An extension of this basic idea to consider the distribution of gradient within the square leads analytically to a Pareto distribution of absorption capacity, given a power distribution of gradient within the square. The probability-distributed model theory (Moore, 1985) can then be used directly to obtain the integrated runoff production for the square for routing to the catchment outlet. justification for the simple linkage function is in part sought through consideration of variants on the basic model where (i) runoff production is based on a topographic index control on saturation and (ii) absorption capacity is related to the Integrated Air Capacity of the soil, as obtained from soil survey. An impervious area fraction is also introduced based on the use of Landsat classified urban areas. The Grid Model and its variants are assessed in Part 2 (Bell and Moore, 1998), first as simulation models and then as forecasting models, following the development of updating procedures to accommodate recent observations of flow so as to improve forecast performance in a real-time context.

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A study on WRF radar data assimilation for hydrological rainfall prediction

Hydrology and Earth System Sciences ◽

10.5194/hess-17-3095-2013 ◽

2013 ◽

Vol 17 (8) ◽

pp. 3095-3110 ◽

Cited By ~ 25

Author(s):

J. Liu ◽

M. Bray ◽

D. Han

Keyword(s):

Data Assimilation ◽

Real Time ◽

Meteorological Data ◽

Weather Radar ◽

Radar Data ◽

Rain Gauge ◽

Radar Reflectivity ◽

Storm Event ◽

Hydrological Applications ◽

Radar Data Assimilation

Abstract. Mesoscale numerical weather prediction (NWP) models are gaining more attention in providing high-resolution rainfall forecasts at the catchment scale for real-time flood forecasting. The model accuracy is however negatively affected by the "spin-up" effect and errors in the initial and lateral boundary conditions. Synoptic studies in the meteorological area have shown that the assimilation of operational observations, especially the weather radar data, can improve the reliability of the rainfall forecasts from the NWP models. This study aims at investigating the potential of radar data assimilation in improving the NWP rainfall forecasts that have direct benefits for hydrological applications. The Weather Research and Forecasting (WRF) model is adopted to generate 10 km rainfall forecasts for a 24 h storm event in the Brue catchment (135.2 km2) located in southwest England. Radar reflectivity from the lowest scan elevation of a C-band weather radar is assimilated by using the three-dimensional variational (3D-Var) data-assimilation technique. Considering the unsatisfactory quality of radar data compared to the rain gauge observations, the radar data are assimilated in both the original form and an improved form based on a real-time correction ratio developed according to the rain gauge observations. Traditional meteorological observations including the surface and upper-air measurements of pressure, temperature, humidity and wind speed are also assimilated as a bench mark to better evaluate and test the potential of radar data assimilation. Four modes of data assimilation are thus carried out on different types/combinations of observations: (1) traditional meteorological data; (2) radar reflectivity; (3) corrected radar reflectivity; (4) a combination of the original reflectivity and meteorological data; and (5) a combination of the corrected reflectivity and meteorological data. The WRF rainfall forecasts before and after different modes of data assimilation are evaluated by examining the rainfall temporal variations and total amounts which have direct impacts on rainfall–runoff transformation in hydrological applications. It is found that by solely assimilating radar data, the improvement of rainfall forecasts are not as obvious as assimilating meteorological data; whereas the positive effect of radar data can be seen when combined with the traditional meteorological data, which leads to the best rainfall forecasts among the five modes. To further improve the effect of radar data assimilation, limitations of the radar correction ratio developed in this study are discussed and suggestions are made on more efficient utilisation of radar data in NWP data assimilation.

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Real-Time Detection and Filtering of Chaff Clutter from Single-Polarization Doppler Radar Data

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-12-00158.1 ◽

2013 ◽

Vol 30 (5) ◽

pp. 873-895 ◽

Cited By ~ 19

Author(s):

Yong Hyun Kim ◽

Sungshin Kim ◽

Hye-Young Han ◽

Bok-Haeng Heo ◽

Cheol-Hwan You

Keyword(s):

Real Time ◽

Doppler Radar ◽

Meteorological Data ◽

Three Dimensional ◽

Weather Radar ◽

Cost Effective ◽

Radar Data ◽

Control Procedure ◽

South Korean ◽

Single Polarization

Abstract In countries with frequent aerial military exercises, chaff particles that are routinely spread by military aircraft represent significant noise sources for ground-based weather radar observation. In this study, a cost-effective procedure is proposed for identifying and removing chaff echoes from single-polarization Doppler radar readings in order to enhance the reliability of observed meteorological data. The proposed quality control procedure is based on three steps: 1) spatial and temporal clustering of decomposed radar image elements, 2) extraction of the clusters’ static and time-evolution characteristics, and 3) real-time identification and removal (or censoring) of target echoes from radar data. Simulation experiments based on this procedure were conducted on site-specific ground-echo-removed weather radar data provided by the Korea Meteorological Administration (KMA), from which three-dimensional (3D) reflectivity echoes covering hundreds of thousands of square kilometers of South Korean territory within an altitude range of 0.25–10 km were retrieved. The algorithm identified and removed chaff clutter from the South Korean data with a novel decision support system at an 81% accuracy level under typical cases in which chaff and weather clusters were isolated from one another with no overlapping areas.

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Statistical and neural classifiers in estimating rain rate from weather radar measurements

Advances in Geosciences ◽

10.5194/adgeo-10-111-2007 ◽

2007 ◽

Vol 10 ◽

pp. 111-115

Author(s):

C. I. Christodoulou ◽

S. C. Michaelides

Keyword(s):

Electromagnetic Radiation ◽

Rain Rate ◽

Weather Radar ◽

Radar Data ◽

Rain Gauge ◽

Meteorological Conditions ◽

Swiss Alps ◽

Rain Gauges ◽

Weather Radars ◽

Radar Measurements

Abstract. Weather radars are used to measure the electromagnetic radiation backscattered by cloud raindrops. Clouds that backscatter more electromagnetic radiation consist of larger droplets of rain and therefore they produce more rain. The idea is to estimate rain rate by using weather radar as an alternative to rain-gauges measuring rainfall on the ground. In an experiment during two days in June and August 1997 over the Italian-Swiss Alps, data from weather radar and surrounding rain-gauges were collected at the same time. The statistical KNN and the neural SOM classifiers were implemented for the classification task using the radar data as input and the rain-gauge measurements as output. The proposed system managed to identify matching pattern waveforms and the rainfall rate on the ground was estimated based on the radar reflectivities with a satisfactory error rate, outperforming the traditional Z/R relationship. It is anticipated that more data, representing a variety of possible meteorological conditions, will lead to improved results. The results in this work show that an estimation of rain rate based on weather radar measurements treated with statistical and neural classifiers is possible.

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Improved radar data processing algorithms for quantitative rainfall estimation in real time

Water Science & Technology ◽

10.2166/wst.2009.282 ◽

2009 ◽

Vol 60 (1) ◽

pp. 175-184 ◽

Cited By ~ 17

Author(s):

S. Krämer ◽

H.-R. Verworn

Keyword(s):

Real Time ◽

Radar Data ◽

Real Time Control ◽

Rainfall Estimation ◽

Radar Rainfall ◽

Rain Gauges ◽

Microphysical Properties ◽

Time Control ◽

Reflectivity Data ◽

Radar Data Processing

This paper describes a new methodology to process C-band radar data for direct use as rainfall input to hydrologic and hydrodynamic models and in real time control of urban drainage systems. In contrast to the adjustment of radar data with the help of rain gauges, the new approach accounts for the microphysical properties of current rainfall. In a first step radar data are corrected for attenuation. This phenomenon has been identified as the main cause for the general underestimation of radar rainfall. Systematic variation of the attenuation coefficients within predefined bounds allows robust reflectivity profiling. Secondly, event specific R–Z relations are applied to the corrected radar reflectivity data in order to generate quantitative reliable radar rainfall estimates. The results of the methodology are validated by a network of 37 rain gauges located in the Emscher and Lippe river basins. Finally, the relevance of the correction methodology for radar rainfall forecasts is demonstrated. It has become clearly obvious, that the new methodology significantly improves the radar rainfall estimation and rainfall forecasts. The algorithms are applicable in real time.

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