To satisfy the needs of the meteorological and aeroecological communities wanting a simple but effective way of flagging each other’s unwanted echo for a variety of different operational radar systems, we evaluated the ability of an estimate of depolarization ratio (DR) based on differential reflectivity (ZDR) and copolar correlation coefficient (ρHV) measurements to separate both types of echoes. The method was tested with data collected by S- and C-band radars used in the United States and Canada. The DR-based method that does not require training achieved 96% separation between weather and biological echoes. Since the misclassifications are typically caused by isolated pixels in the melting layer or at the edge of echo patterns, the addition of a despeckling algorithm considerably reduces further these false alarms, resulting in an increase in correct identification approaching 99% on test cases.

occurrence, formation, and decay? What is the climatology of severe weather? Answers to these 28 questions have value on their own and also invariably trigger more questions about the processes 29 causing these patterns as well as suggest some answers. They also have considerable pedagogical 30 value to illustrate in the classroom the impacts on precipitation of different processes such as 31 sea-land breezes, topography, and seasons. 32In this work, U.S. mosaics of radar data from 1996 to 2015 are used to demonstrate the 33 possibilities offered by such a data set. Three topics are touched: a) climatologies and daily 34 cycles of precipitation and convection, and what they can teach us about precipitation 35 mechanisms; b) the spatial and temporal distribution of the appearance and occurrence of 36convection, and what it reveals on the importance of surface terrain properties for these events; 37 and c) the power and challenges of looking for a small signal in even such a large dataset using 38 the influence of weekly activity cycles and of cities on precipitation as an illustration. 39 CAPSULE 40Where the radar climatology of weather echoes is used to reveal how surface properties 41 shape precipitation occurrence and to explore the ease or difficulty to unambiguously detect 42 these effects. 43 3 Precipitation climatology and radars 44Radar has historically transformed the way we study storms thanks to its ability to take 45 frequent and regular 3-D measurements even through clouds and precipitation. As a result, it is 46 commonly used both operationally for weather surveillance as well as for research to help 47 understand the dynamics and microphysical processes of atmospheric phenomena (Atlas et al. 48 1990; Wakimoto and Srivastava 2003; Fabry 2015). 49The first national Doppler radar network in the world was deployed in the United States 50 in the mid-1990s. More importantly, a framework and process for monitoring and maintaining 51 radar data quality was implemented and adhered to since. From late 1995 onwards, the 52 reflectivity data from all these radars have been made into national mosaics by a variety of 53 actors, including private companies, research institutes, and the National Weather Service itself. 54A unique dataset now exists to study radar echoes collected by the same radars over a period of 55 more than 20 years (and counting) over the contiguous United States. 56Though country-wide climatological information on precipitation exists, for example 57 from the US Climate Normals (Applequist et al. 2012, Arguez et al. 2012, the information 58 available is not as rich as it could be. As an illustration, we challenge all readers to find the 59 These questions both complicate the interpretation of a radar echo climatology and can also be 88 partially answered by it (see the sidebar on Data, Processing, and Quality Issues for some 89 details). In parallel, radar has unique strengths, in particular for measuring the coverage and 90 instantaneous intensity of precipitation, more so than for quant...

Abstract. More so than the traditional fixed radiometers, the scanning radiometer requires a careful design to ensure high quality measurements. Here the impact of the radiometer characteristics (e.g., antenna beam width and receiver bandwidth) and atmospheric propagation (e.g. curvature of the Earth and vertical gradient of refractive index) on scanning radiometer measurements are presented. A forward radiative transfer model that includes all these effects to represent the instrument measurements is used to estimate the biases. These biases are estimated using differences between the measurement with and without these characteristics for three commonly used frequency bands: K, V and W-band. The receiver channel bandwidth errors are less important in K-band and W-band. Thus, the use of a wider bandwidth to improve detection at low signal-to-noise conditions is acceptable at these frequencies. The biases caused by omitting the antenna beam width in measurement simulations are larger than those caused by omitting the receiver bandwidth, except for V-band where the bandwidth may be more important in the vicinity of absorption peaks. Using simple regression algorithms, the effects of the bandwidth and beam width biases in liquid water path, integrated water vapour, and temperature are also examined. The largest errors in liquid water path and integrated water vapour are associated with the beam width errors.

Although radar is our most useful tool for monitoring severe weather, the benefits of assimilating its data are often short lived. To understand why, we documented the assimilation requirements, the data characteristics, and the common practices that could hinder optimum data assimilation by traditional approaches. Within storms, radars provide dense measurements of a few highly variable storm outcomes (precipitation and wind) in atmospherically unstable conditions. However, statistical relationships between errors of observed and unobserved quantities often become nonlinear because the errors in these areas tend to become large rapidly. Beyond precipitating areas lie large regions for which radars provide limited new information, yet whose properties will soon shape the outcome of future storms. For those areas, any innovation must consequently be projected from sometimes distant precipitating areas. Thus, radar data assimilation must contend with a double need at odds with many traditional assimilation implementations: correcting in-storm properties with complex errors while projecting information at unusually far distances outside precipitating areas. To further complicate the issue, other data properties and practices, such as assimilating reflectivity in logarithmic units, are not optimal to correct all state variables. Therefore, many characteristics of radar measurements and common practices of their assimilation are incompatible with necessary conditions for successful data assimilation. Facing these dataset-specific challenges may force us to consider new approaches that use the available information differently.

Two-dimensional water vapor fields were retrieved by simulated measurements from multiple groundbased microwave radiometers using a tomographic approach. The goal of this paper was to investigate how the various aspects of the instrument setup (number and spacing of elevation angles and of instruments, number of frequencies, etc.) affected the quality of the retrieved field. This was done for two simulated atmospheric water vapor fields: 1) an exaggerated turbulent boundary layer and 2) a simplified water vapor front. An optimal estimation algorithm was used to obtain the tomographic field from the microwave radiometers and to evaluate the fidelity and information content of this retrieved field.While the retrieval of the simplified front was reasonably successful, the retrieval could not reproduce the details of the turbulent boundary layer field even using up to nine instruments and 25 elevation angles. In addition, the vertical profile of the variability of the water vapor field could not be captured. An additional set of tests was performed using simulated data from a Raman lidar. Even with the detailed lidar measurements, the retrieval did not succeed except when the lidar data were used to define the a priori covariance matrix. This suggests that the main limitation to obtaining fine structures in a retrieved field using tomographic retrievals is the definition of the a priori covariance matrix.