Characterizing the errors in satellite-based precipitation estimation products is crucial for understanding their effects in hydrological applications. Six precipitation products derived from three algorithms are comprehensively evaluated against gauge data over mainland China from December 2006 to November 2010. These products include three satellite-only estimates: the Global Satellite Mapping of Precipitation Microwave-IR Combined Product (GSMaP_MVK), the Climate Prediction Center (CPC) MORPHing (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), as well as their gauge-corrected counterparts: the GSMaP Gauge-calibrated Product (GSMaP_Gauge), bias-corrected CMORPH (CMORPH_CRT), and PERSIANN Climate Data Record (PERSIANN-CDR). Overall, the bias-correction procedures largely reduce various errors for the three groups of satellite-based precipitation products. GSMaP_Gauge produces better fractional coverage with the highest correlation (0.95) and the lowest RMSE (0.53 mm/day) but also high RB (15.77%). In general, CMORPH_CRT amounts are closer to the gauge reference. CMORPH shows better performance than GSMaP_MVK and PERSIANN with the highest CC (0.82) and the lowest RMSE (0.93 mm/day), but also presents a relatively high RB (−19.60%). In winter, all six satellite precipitation estimates have comparatively poor capability, with the IR-based PERSIANN_CDR exhibiting the closest performance to the gauge reference. Both satellite-only and gauge-corrected satellite products show poor capability in detecting occurrence of precipitation with a low POD (<50%) and CSI (40%).

Bats provide a wealth of vital services to humans, such as pollinating plants and managing populations of insects that act as agricultural pests and disease vectors. Despite their direct importance, it is inherently difficult to monitor bats across large spatiotemporal scales, in part due to their nocturnal activity and long‐distance, high‐altitude flights. Networks of weather surveillance radars provide continuous measurements of the airspace at continental scales, revealing the abundance, distribution, behaviour, and phenology of bats aloft. Using a network of polarimetric Doppler weather radars in the USA, we demonstrate applications of this technology to monitor large bat colonies, highlight recent discoveries made using radar surveillance of bats, and discuss future prospects for extending these techniques as part of a standardised system of global bat monitoring infrastructure.

The high mobility of airborne organisms makes them inherently difficult to study, motivating the use of radars and radar networks as biological surveillance tools. While the utility of radar for ecological studies has been demonstrated, a number of challenges remain in expanding and optimizing their use for surveillance of birds, bats and insects. To explore these topics, a Lagrangian simulation scheme has been developed to synthesize realistic, polarimetric, pulsed Doppler radar baseband signals from modelled flocks of biological point scatterers. This radar simulation algorithm is described, and an application is presented using an agent‐based model of the nocturnal emergence of a cave‐dwelling colony of Brazilian free‐tailed bats (Tadarida brasiliensis). Dual‐polarization radar signals for an S‐band weather surveillance radar are synthesized and used to develop a new extension of the spectral velocity azimuth display for polarimetric roost‐ring signature analysis, demonstrating one capability of this simulation scheme. While these developments will have direct benefits for radar engineers and meteorologists, continuing investment in radar methods such as these will have cascading effects toward improving ecological models and developing new observational techniques for monitoring aerial wildlife.