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Aart Overeem, Remko Uijlenhoet, and Hidde Leijnse

Abstract

The Royal Netherlands Meteorological Institute (KNMI) operates two dual-polarization C-band weather radars in simultaneous transmission and reception (STAR; i.e., horizontally and vertically polarized pulses are transmitted simultaneously) mode, providing 2D radar rainfall products. Despite the application of Doppler and speckle filtering, remaining nonmeteorological echoes (especially sea clutter) mainly due to anomalous propagation still pose a problem. This calls for additional filtering algorithms, which can be realized by means of polarimetry. Here we explore the effectiveness of the open-source wradlib fuzzy echo classification and clutter identification based on polarimetric moments. Based on our study, this has recently been extended with the depolarization ratio and clutter phase alignment as new decision variables. Optimal values for weights of the different membership functions and threshold are determined employing a 4-h calibration dataset from one radar. The method is applied to a full year of volumetric data from the two radars in the Dutch temperate climate. The verification focuses on the presence of remaining nonmeteorological echoes by mapping the number of exceedances of radar reflectivity factors for given thresholds. Moreover, accumulated rainfall maps are obtained to detect unrealistically large rainfall depths. The results are compared to those for which no further filtering has been applied. Verification against rain gauge data reveals that only a little precipitation is removed. Because the fuzzy logic algorithm removes many nonmeteorological echoes, the practice to composite data from both radars in logarithmic space to hide these echoes is abandoned and replaced by linearly averaging reflectivities.

Free access
Hidde Leijnse, Remko Uijlenhoet, and Alexis Berne

Abstract

Microwave links can be used for the estimation of path-averaged rainfall by using either the path-integrated attenuation or the difference in attenuation of two signals with different frequencies and/or polarizations. Link signals have been simulated using measured time series of raindrop size distributions (DSDs) over a period of nearly 2 yr, in combination with wind velocity data and Taylor’s hypothesis. For this purpose, Taylor’s hypothesis has been tested using more than 1.5 yr of high-resolution radar data. In terms of correlation between spatial and temporal profiles of rainfall intensities, the validity of Taylor’s hypothesis quickly decreases with distance. However, in terms of error statistics, the hypothesis is seen to hold up to distances of at least 10 km. Errors and uncertainties (mean bias error and root-mean-square error, respectively) in microwave link rainfall estimates due to spatial DSD variation are at a minimum at frequencies (and frequency combinations) where the power-law relation for the conversion to rainfall intensity is close to linear. Errors generally increase with link length, whereas uncertainties decrease because of the decrease of scatter about the retrieval relations because of averaging of spatially variable DSDs for longer links. The exponent of power-law rainfall retrieval relations can explain a large part of the variation in both bias and uncertainty, which means that the order of magnitude of these error statistics can be predicted from the value of this exponent, regardless of the link length.

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Linda Bogerd, Aart Overeem, Hidde Leijnse, and Remko Uijlenhoet

Abstract

Applications like drought monitoring and forecasting can profit from the global and near-real-time availability of satellite-based precipitation estimates once their related uncertainties and challenges are identified and treated. To this end, this study evaluates the IMERG V06B Late Run precipitation product from the Global Precipitation Measurement mission (GPM), a multisatellite product that combines space-based radar, passive microwave (PMW), and infrared (IR) data into gridded precipitation estimates. The evaluation is performed on the spatiotemporal resolution of IMERG (0.1° × 0.1°, 30 min) over the Netherlands over a 5-yr period. A gauge-adjusted radar precipitation product from the Royal Netherlands Meteorological Institute (KNMI) is used as reference, against which IMERG shows a large positive bias. To find the origin of this systematic overestimation, the data are divided into seasons, rainfall intensity ranges, echo top height (ETH) ranges, and categories based on the relative contributions of IR, morphing, and PMW data to the IMERG estimates. Furthermore, the specific radiometer is identified for each PMW-based estimate. IMERG’s detection performance improves with higher ETH and rainfall intensity, but the associated error and relative bias increase as well. Severe overestimation occurs during low-intensity rainfall events and is especially linked to PMW observations. All individual PMW instruments show the same pattern: overestimation of low-intensity events and underestimation of high-intensity events. IMERG misses a large fraction of shallow rainfall events, which is amplified when IR data are included. Space-based retrieval of shallow and low-intensity precipitation events should improve before IMERG can become accurate over the middle and high latitudes.

Restricted access
Thomas C. van Leth, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet

Abstract

We investigate the spatiotemporal structure of rainfall at spatial scales from 7 m to over 200 km in the Netherlands. We used data from two networks of laser disdrometers with complementary interstation distances in two Dutch cities (comprising five and six disdrometers, respectively) and a Dutch nationwide network of 31 automatic rain gauges. The smallest aggregation interval for which raindrop size distributions were collected by the disdrometers was 30 s, while the automatic rain gauges provided 10-min rainfall sums. This study aims to supplement other micro-γ investigations (usually performed in the context of spatial rainfall variability within a weather radar pixel) with new data, while characterizing the correlation structure across an extended range of scales. To quantify the spatiotemporal variability, we employ a two-parameter exponential model fitted to the spatial correlograms and characterize the parameters of the model as a function of the temporal aggregation interval. This widely used method allows for a meaningful comparison with seven other studies across contrasting climatic settings all around the world. We also separately analyzed the intermittency of the rainfall observations. We show that a single parameterization, consisting of a two-parameter exponential spatial model as a function of interstation distance combined with a power-law model for decorrelation distance as a function of aggregation interval, can coherently describe rainfall variability (both spatial correlation and intermittency) across a wide range of scales. Limiting the range of scales to those typically found in micro-γ variability studies (including four of the seven studies to which we compare our results) skews the parameterization and reduces its applicability to larger scales.

Open access
Elena Saltikoff, Mikko Kurri, Hidde Leijnse, Sergio Barbosa, and Kjetil Stiansen

Abstract

Weather radars provide us with colorful images of storms, their development, and their movement, but from time to time the radars fail and we are left without data. To minimize these disruptions, owners of weather radars carry out preventive maintenance.

The European radar project Operational Programme for the Exchange of Weather Radar Information (OPERA) conducted a survey among technicians from 21 countries on their experiences of maintenance. Regular maintenance frequency varies widely from as frequent as weekly to as infrequent as 6 months. Results show that the primary causes of missing data are not the failure of radar components and software or lack of maintenance but rather issues with the electricity supplies or telecommunications. Where issues are with the radars themselves, they are most commonly with the transmitter or the antenna controllers. Faults can be repaired quickly, but, if certain parts are required or the site is very remote, a radar can be out of service for weeks or even months. Failures of electricity or communications may also lead to lengthy periods of unavailability. As an example there is a story from Norway where wintertime thunderstorms severely damaged a radar at a very remote location.

Annual operative costs of a radar are typically on the order of 5%–10% of the radar purchase price. During the lifetime of a system (typically 10–20 years) the operator can hence pay as much for the running costs as for the hardware purchase. It is extremely important to take infrastructure, maintenance, and monitoring into account when purchasing a new radar.

Open access
Asko Huuskonen, Mikko Kurri, Harri Hohti, Hans Beekhuis, Hidde Leijnse, and Iwan Holleman

Abstract

A method for the operational monitoring of the weather radar antenna mechanics and signal processing is presented. The method is based on the analysis of sun signals in the polar volume data produced during the operational scanning of weather radars. Depending on the hardware of the radar, the volume coverage pattern, the season, and the latitude of the radar, several tens of sun hits are found per day. The method is an extension of that for determining the weather radar antenna pointing and for monitoring the receiver stability and the differential reflectivity offset. In the method the width of the sun image in elevation and in azimuth is analyzed from the data, together with the center point position and the total power, analyzed in the earlier methods. This paper describes how the width values are obtained in the majority of cases without affecting the quality of the position and power values. Results from the daily analysis reveal signal processing features and failures that are difficult to find out otherwise in weather data. Moreover, they provide a tool for monitoring the stability of the antenna system, and hence the method has great potential for routine monitoring of radar signal processing and the antenna mechanics. Hence, it is recommended that the operational solar analysis be extended into the analysis of the width.

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Aart Overeem, Hylke de Vries, Hassan Al Sakka, Remko Uijlenhoet, and Hidde Leijnse

Abstract

The Royal Netherlands Meteorological Institute (KNMI) operates two operational dual-polarization C-band weather radars providing 2D radar rainfall products. Attenuation can result in severe underestimation of rainfall amounts, particularly in convective situations that are known to have high impact on society. To improve the radar-based precipitation estimates, two attenuation correction methods are evaluated and compared: 1) modified Kraemer (MK) method, i.e., Hitschfeld–Bordan where parameters of the power-law Z hk h relation are adjusted such that reflectivities in the entire dataset do not exceed 59 dBZ h and attenuation correction is limited to 10 dB; and 2) using two-way path-integrated attenuation computed from the dual-polarization moment specific differential phase K dp (Kdp method). In both cases the open-source Python library wradlib is employed for the actual attenuation correction. A radar voxel only contributes to the computed path-integrated attenuation if its height is below the forecasted freezing-level height from the numerical weather prediction model HARMONIE-AROME. The methods are effective in improving hourly and daily quantitative precipitation estimation (QPE), where the Kdp method performs best. The verification against rain gauge data shows that the underestimation diminishes from 55% to 37% for hourly rainfall for the Kdp method when the gauge indicates more than 10 mm of rain in that hour. The improvement for the MK method is less pronounced, with a resulting underestimation of 40%. The stability of the MK method holds a promise for application to data archives from single-polarization radars.

Open access
Marielle Gosset, Harald Kunstmann, François Zougmore, Frederic Cazenave, Hidde Leijnse, Remko Uijlenhoet, Christian Chwala, Felix Keis, Ali Doumounia, Barry Boubacar, Modeste Kacou, Pinhas Alpert, Hagit Messer, Jörg Rieckermann, and Joost Hoedjes

FIRST INTERNATIONAL WORKSHOP ON RAINFALL MEASUREMENT FROM CELLULAR PHONE NETWORKS IN AFRICA (RAIN CELL AFRICA)

What: Eighty-seven participants from 18 countries met to discuss the prospect for rainfall measurement and high-resolution mapping based on commercial microwave links in Africa. Experts from Europe and Israel provided training to African students, scientists, and meteorologists on this innovative method.

When: 30 March–2 April 2015

Where: Ouagadougou, Burkina Faso

The International Workshop on Rainfall Measurement Based on Microwave (MW) links from Commercial Cellular Communication Networks (Rain Cell) was held in Ouagadougou, Burkina Faso, from 30 March to 2 April 2015 (

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Judy Shamoun-Baranes, Silke Bauer, Jason W. Chapman, Peter Desmet, Adriaan M. Dokter, Andrew Farnsworth, Hans van Gasteren, Birgen Haest, Jarmo Koistinen, Bart Kranstauber, Felix Liechti, Tom H. E. Mason, Cecilia Nilsson, Raphael Nussbaumer, Baptiste Schmid, Nadja Weisshaupt, and Hidde Leijnse

Abstract

Weather radar networks have great potential for continuous and long-term monitoring of aerial biodiversity of birds, bats, and insects. Biological data from weather radars can support ecological research, inform conservation policy development and implementation, and increase the public’s interest in natural phenomena such as migration. Weather radars are already used to study animal migration, quantify changes in populations, and reduce aerial conflicts between birds and aircraft. Yet efforts to establish a framework for the broad utilization of operational weather radar for biodiversity monitoring are at risk without suitable data policies and infrastructure in place. In Europe, communities of meteorologists and ecologists have made joint efforts toward sharing and standardizing continent-wide weather radar data. These efforts are now at risk as new meteorological data exchange policies render data useless for biodiversity monitoring. In several other parts of the world, weather radar data are not even available for ecological research. We urge policy makers, funding agencies, and meteorological organizations across the world to recognize the full potential of weather radar data. We propose several actions that would ensure the continued capability of weather radar networks worldwide to act as powerful tools for biodiversity monitoring and research.

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