@article{tomkins_yuter_miller_2024, title={Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms}, volume={17}, ISSN={["1867-8548"]}, url={https://doi.org/10.5194/amt-17-3377-2024}, DOI={10.5194/amt-17-3377-2024}, abstractNote={Abstract. Radar observations of winter storms often exhibit locally enhanced linear features in reflectivity, sometimes labeled as snow bands. We have developed a new, objective method for detecting locally enhanced echo features in radar data from winter storms. In comparison to convective cells in warm season precipitation, these features are usually less distinct from the background echo and often have more fuzzy or feathered edges. This technique identifies both prominent, strong features and more subtle, faint features. A key difference from previous radar reflectivity feature detection algorithms is the combined use of two adaptive differential thresholds, one that decreases with increasing background values and one that increases with increasing background values. The algorithm detects features within a snow rate field rather than reflectivity and incorporates an underestimate and overestimate of feature areas to account for uncertainties in the detection. We demonstrate the technique on several examples from the US National Weather Service operational radar network. The feature detection algorithm is highly customizable and can be tuned for a variety of data sets and applications.}, number={11}, journal={ATMOSPHERIC MEASUREMENT TECHNIQUES}, author={Tomkins, Laura M. and Yuter, Sandra E. and Miller, Matthew A.}, year={2024}, month={Jun}, pages={3377–3399} }
 @article{ue_olle_uter_ollias_eh_omkins_2024, title={Microscale Updrafts within Northeast US Coastal Snowstorms Using High-Resolution Cloud Radar Measurements}, volume={152}, ISSN={["1520-0493"]}, DOI={10.1175/MWR-D-23-0055.1}, abstractNote={Abstract}, number={3}, journal={MONTHLY WEATHER REVIEW}, author={Ue, M. ariko o and Olle, Rian a. c and Uter, S. andra e. y and Ollias, P. avlos k and Eh, P. hillip y and Omkins, L. aura m. t}, year={2024}, month={Mar}, pages={865–889} }
 @article{allen_yuter_miller_tomkins_2024, title={Objective identification of pressure wave events from networks of 1 Hz, high-precision sensors}, volume={17}, ISSN={["1867-8548"]}, url={https://doi.org/10.5194/amt-17-113-2024}, DOI={10.5194/amt-17-113-2024}, abstractNote={Abstract. Mesoscale pressure waves, including atmospheric gravity waves, outflow and frontal passages, and wake lows, are outputs of and can potentially modify clouds and precipitation. The vertical motions associated with these waves can modify the temperature and relative humidity of air parcels and thus yield potentially irreversible changes to the cloud and precipitation content of those parcels. A wavelet-based method for identifying and tracking these types of wave signals in time series data from networks of low-cost, high-precision (0.8 Pa noise floor, 1 Hz recording frequency) pressure sensors is demonstrated. Strong wavelet signals are identified using a wave-period-dependent (i.e., frequency-dependent) threshold, and then those signals are extracted by inverting the wavelet transform. Wave periods between 1 and 120 min were analyzed – a range which could capture acoustic, acoustic-gravity, and gravity wave modes. After extracting the signals from a network of pressure sensors, the cross-correlation function is used to estimate the time difference between the wave passage at each pressure sensor. From those time differences, the wave phase velocity vector is calculated using a least-squares fit. If the fitting error is sufficiently small (thresholds of RMSE < 90 s and NRMSE < 0.1 were used), then a wave event is considered robust and trackable. We present examples of tracked wave events, including a Lamb wave caused by the Hunga Tonga volcanic eruption in January 2020, a gravity wave train, an outflow boundary passage, a frontal passage, and a cold front passage. The data and processing techniques presented here can have research applications in wave climatology and testing associations between waves and atmospheric phenomena.
                    }, number={1}, journal={ATMOSPHERIC MEASUREMENT TECHNIQUES}, author={Allen, Luke R. and Yuter, Sandra E. and Miller, Matthew A. and Tomkins, Laura M.}, year={2024}, month={Jan}, pages={113–134} }
 @article{miller_yuter_hoban_tomkins_colle_2022, title={Detecting wave features in Doppler radial velocity radar observations}, volume={15}, ISSN={["1867-8548"]}, url={https://doi.org/10.5194/amt-15-1689-2022}, DOI={10.5194/amt-15-1689-2022}, abstractNote={Abstract. Mesoscale, wave-like perturbations in horizontal air motions in the troposphere (velocity waves) are associated with vertical velocity, temperature, and pressure perturbations that can initiate or enhance precipitation within clouds. The ability to detect velocity waves from horizontal wind information is an important tool for atmospheric research and weather forecasting. This paper presents a method to routinely detect velocity waves using Doppler radial velocity data from a scanning weather radar. The method utilizes the difference field between consecutive position plan indicator (PPI) scans at a given elevation angle. Using the difference between fields a few minutes apart highlights small-scale perturbations associated with waves because the larger-scale wind field changes more slowly. Image filtering retains larger contiguous velocity bands and discards noise. Wave detection scales are limited by the size of the temporal difference relative to the wave motion and the radar resolution volume size.
                    }, number={6}, journal={ATMOSPHERIC MEASUREMENT TECHNIQUES}, publisher={Copernicus GmbH}, author={Miller, Matthew A. and Yuter, Sandra E. and Hoban, Nicole P. and Tomkins, Laura M. and Colle, Brian A.}, year={2022}, month={Mar}, pages={1689–1702} }
 @article{tomkins_yuter_miller_allen_2022, title={Image muting of mixed precipitation to improve identification of regions of heavy snow in radar data}, volume={15}, ISSN={["1867-8548"]}, url={https://doi.org/10.5194/amt-15-5515-2022}, DOI={10.5194/amt-15-5515-2022}, abstractNote={Abstract. In winter storms, enhanced radar reflectivity is often associated with heavy snow. However, some higher reflectivities are the result of mixed precipitation including melting snow. The correlation coefficient (a dual-polarization radar variable) can identify regions of mixed precipitation, but this information is usually presented separately from reflectivity. Especially under time pressure, radar data users can mistake regions of mixed precipitation for heavy snow because of the high cognitive load associated with comparing data in two fields while simultaneously attempting to discount a portion of the high reflectivity values. We developed an image muting method for regional radar maps that visually de-emphasizes the high reflectivity values associated with mixed precipitation. These image muted depictions of winter storm precipitation structures are useful for analyzing regions of heavy snow and monitoring real-time weather conditions.}, number={18}, journal={ATMOSPHERIC MEASUREMENT TECHNIQUES}, author={Tomkins, Laura M. and Yuter, Sandra E. and Miller, Matthew A. and Allen, Luke R.}, year={2022}, month={Sep}, pages={5515–5525} }
 @article{tomkins_mechem_yuter_rhodes_2021, title={Regional Flow Conditions Associated with Stratocumulus Cloud-Eroding Boundaries over the Southeast Atlantice}, volume={149}, ISSN={["1520-0493"]}, DOI={10.1175/MWR-D-20-0250.1}, abstractNote={Abstract}, number={6}, journal={MONTHLY WEATHER REVIEW}, author={Tomkins, Laura M. and Mechem, David B. and Yuter, Sandra E. and Rhodes, Spencer R.}, year={2021}, month={Jun}, pages={1903–1917} }
 @article{yoshizumi_coffer_collins_gaines_gao_jones_mcgregor_mcquillan_perin_tomkins_et al._2020, title={A Review of Geospatial Content in IEEE Visualization Publications}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85100716572&partnerID=MN8TOARS}, DOI={10.1109/VIS47514.2020.00017}, abstractNote={Geospatial analysis is crucial for addressing many of the world’s most pressing challenges. Given this, there is immense value in improving and expanding the visualization techniques used to communicate geospatial data. In this work, we explore this important intersection – between geospatial analytics and visualization – by examining a set of recent IEEE VIS Conference papers (a selection from 2017-2019) to assess the inclusion of geospatial data and geospatial analyses within these papers. After removing the papers with no geospatial data, we organize the remaining literature into geospatial data domain categories and provide insight into how these categories relate to VIS Conference paper types. We also contextualize our results by investigating the use of geospatial terms in IEEE Visualization publications over the last 30 years. Our work provides an understanding of the quantity and role of geospatial subject matter in recent IEEE VIS publications and supplies a foundation for future meta-analytical work around geospatial analytics and geovisualization that may shed light on opportunities for innovation.}, journal={2020 IEEE VISUALIZATION CONFERENCE - SHORT PAPERS (VIS 2020)}, author={Yoshizumi, Alexander and Coffer, Megan M. and Collins, Elyssa L. and Gaines, Mollie D. and Gao, Xiaojie and Jones, Kate and McGregor, Ian R. and McQuillan, Katie A. and Perin, Vinicius and Tomkins, Laura M. and et al.}, year={2020}, pages={51–55} }