The Effects of Spatial Interpolation on a Novel, Dual-Doppler 3D Wind Retrieval Technique

Three-dimensional wind retrievals from ground-based Doppler radars have played an important role in meteorological research and nowcasting over the past four decades. However, in recent years, the proliferation of open-source software and increased demands from applications such as convective parameterizations in numerical weather prediction models has led to a renewed interest in these analyses. In this study, we analyze how a major, yet often-overlooked, error source effects the quality of retrieved 3D wind fields. Namely, we investigate the effects of spatial interpolation, and show how the common practice of pre-gridding radial velocity data can degrade the accuracy of the results. Alternatively, we show that assimilating radar data directly at their observation locations improves the retrieval of important dynamic features such as the rear flank downdraft and mesocyclone within a simulated supercell, while also reducing errors in vertical vorticity, horizontal divergence, and all three velocity components.Comment: Revised version submitted to JTECH. Includes new section with a real data cas

The development of rainfall retrievals from radar at Darwin

17 USC 105 interim-entered record; under review.The article of record as published may be found at https://doi.org/10.5194/amt-14-53-2021The U.S. Department of Energy Atmospheric Radiation Measurement program Tropical Western Pacific site hosted a C-band polarization (CPOL) radar in Darwin, Australia. It provides 2 decades of tropical rainfall characteristics useful for validating global circulation models. Rainfall retrievals from radar assume characteristics about the droplet size distribution (DSD) that vary significantly. To minimize the uncertainty associated with DSD variability, new radar rainfall techniques use dual polarization and specific attenuation estimates. This study challenges the applicability of several specific attenuation and dual-polarization-based rainfall estimators in tropical settings using a 4-year archive of Darwin disdrometer datasets in conjunction with CPOL observations. This assessment is based on three metrics: statistical uncertainty estimates, principal component analysis (PCA), and comparisons of various retrievals from CPOL data. The PCA shows that the variability in R can be consistently attributed to reflectivity, but dependence on dualpolarization quantities was wavelength dependent for 1 10 mm h−1 . Rainfall estimates during these conditions primarily originate from deep convective clouds with median drop diameters greater than 1.5 mm. An uncertainty analysis and intercomparison with CPOL show that a Colorado State University blended technique for tropical oceans, with modified estimators developed from video disdrometer observations, is most appropriate for use in all cases, such as when 1 10 mm h−1 (deeper convective rain).Argonne National Laboratory’s work was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, under contract DE-AC02-06CH11357. This work has been supported by the Office of Biological and Environmental Research (OBER) of the U.S. Department of Energy (DOE) as part of the Climate Model Development and Validation activity. NOAA PSL contributes effort with funding from the Weather Program Office’s Precipitation Prediction Grand Challenge. The development of the Python ARM radar toolkit was funded by the ARM program part of the Office of Biological and Environmental Research (OBER) of the U.S. Department of Energy (DOE). The work from Monash University and the Bureau of Meteorology was partly supported by the U.S. Department of Energy Atmospheric Systems Research Program through the grant DE-SC0014063. BD contributions are supported by the U.S. Department of Energy Atmospheric Systems Research Program through the grant DE-SC0017977

Analysis of Blue Corona Discharges at the Top of Tropical Thunderstorm Clouds in Different Phases of Convection

We report on observations of corona discharges at the uppermost region of clouds characterized by emissions in a blue band of nitrogen molecules at 337 nm, with little activity in the red band of lightning leaders at 777.4 nm. Past work suggests that they are generated in cloud tops reaching the tropopause and above. Here we explore their occurrence in two convective environments of the same storm: one is developing with clouds reaching above the tropopause, and one is collapsing with lower cloud tops. We focus on those discharges that form a distinct category with rise times below 20 μs, implying that they are at the very top of the clouds. The discharges are observed in both environments. The observations suggest that a range of storm environments may generate corona discharges and that they may be common in convective surges.publishedVersio

BASTA : a 95GHz FM-­‐CW Cloud radar

International audienceGround-based continuous observation of non-precipitating clouds and fo

Atmospheric convection and air-sea interactions over the tropical oceans: scientific progress, challenges, and opportunities

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hagos, S., Foltz, G. R., Zhang, C., Thompson, E., Seo, H., Chen, S., Capotondi, A., Reed, K. A., DeMott, C., & Protat, A. Atmospheric convection and air-sea interactions over the tropical oceans: scientific progress, challenges, and opportunities. Bulletin of the American Meteorological Society, 101(3), (2020): E253-E258, doi:10.1175/BAMS-D-19-0261.1.Over the past 30 years, the scientific community has made considerable progress in understanding and predicting tropical convection and air–sea interactions, thanks to sustained investments in extensive in situ and remote sensing observations, targeted field experiments, advances in numerical modeling, and vastly improved computational resources and observing technologies. Those investments would not have been fruitful as isolated advancements without the collaborative effort of the atmospheric convection and air–sea interaction research communities. In this spirit, a U.S.- and International CLIVAR–sponsored workshop on “Atmospheric convection and air–sea interactions over the tropical oceans” was held in the spring of 2019 in Boulder, Colorado. The 90 participants were observational and modeling experts from the atmospheric convection and air–sea interactions communities with varying degrees of experience, from early-career researchers and students to senior scientists. The presentations and discussions covered processes over the broad range of spatiotemporal scales (Fig. 1).The workshop was sponsored by the United States and International CLIVAR. Funding was provided by the U.S. Department of Energy, Office of Naval Research, NOAA, NSF, and the World Climate Research Programme. We thank Mike Patterson, Jennie Zhu, and Jeff Becker from the U.S. CLIVAR Project Office for coordinating the workshop

Detecting range and coupling coefficient tradeoff with a multiple loops reader antenna for small size RFID LF tags

International audienceThis paper summarizes some tests with Low Frequency (LF, 125 kHz) RFID tags of two types: Card and Token. These tests were done in order to evaluate the feasibility of an identification/traceability of tags which size is constrained and supposed to be detected inside a delimited volume of 40×40×10 cm 3 . As the size of the antenna tag is supposed to be very small, we improve the detection range and volume of definition by designing different reader antennas. Reader antennas presented are of two types whether they are based on single (SL) or multiple loops (ML). Detection range was evaluated for planar antennas (3 SL and one ML). Volume of definition for the detection was estimated by designing two-level prototypes of ML antennas. Results are discussed about the optimization possibility of detection range and volume thanks to ML

A machine learning assisted development of a model for the populations of convective and stratiform clouds

Traditional parameterizations of the interaction between convection and the environment have relied on an assumption that the slowly-varying large-scale environment is in statistical equilibrium with a large number of small and short-lived convective clouds. They fail to capture non-equilibrium transitions such as the diurnal cycle and theformation of meso-scale convective systems as well asobserved precipitation statisticsand extremes. Informed by analysis of radar observations, cloud-permitting model simulation, theory and machine learning, this work presents a new stochastic cloud population dynamics model for characterizing the interactions between convective and stratiform clouds,with the ultimate goal of informing the representation ofthese interactions in global climate models. 15 wet seasons of precipitating cloud observations by a C-band radar at Darwin, Australia are fed into a machine learning algorithm to obtain transition functions that close a set of coupled equation relating large-scale forcing, mass flux, the convective cell size distribution and the stratiform area. Under realistic large-scale forcing, the derived transition functions show that, on the one hand, interactions with stratiform clouds act to dampen the variability in the size and number of convective cells and therefore in the convective mass flux. On the other hand, for a given convective area fraction, a larger number of smaller cells is more favorable for the growth of stratiform area than a smaller number of larger cells. The combination of these two factors gives rise to solutions with a number of convective cells embedded in a large stratiform area, reminiscent of mesoscale convective systems

An Integrated Approach to Weather Radar Calibration and Monitoring Using Ground Clutter and Satellite Comparisons

The stability and accuracy of weather radar reflectivity calibration are imperative for quantitative applications, such as rainfall estimation, severe weather monitoring and nowcasting, and assimilation in numerical weather prediction models. Various radar calibration and monitoring techniques have been developed, but only recently have integrated approaches been proposed, that is, using different calibration techniques in combination. In this paper the following three techniques are used: 1) ground clutter monitoring, 2) comparisons with spaceborne radars, and 3) the self-consistency of polarimetric variables. These techniques are applied to a C-band polarimetric radar (CPOL) located in the Australian tropics since 1998. The ground clutter monitoring technique is applied to each radar volumetric scan and provides a means to reliably detect changes in calibration, relative to a baseline. It is remarkably stable to within a standard deviation of 0.1 dB (decibels). To obtain an absolute calibration value, CPOL observations are compared to spaceborne radars on board TRMM (Tropical Rainfall Measuring Mission) and GPM (Global Precipitation Measurement) using a volume-matching technique. Using an iterative procedure and stable calibration periods identified by the ground echoes technique, we improve the accuracy of this technique to about 1 dB. Finally, we review the self-consistency technique and constrain its assumptions using results from the hybrid TRMM-GPM and ground echo technique. Small changes in the self-consistency parameterization can lead to 5 dB of variation in the reflectivity calibration. We find that the drop-shape model of Brandes et al. with a standard deviation of the canting angle of 12 degrees best matches our dataset