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1. Combined Assimilation of Doppler Wind Lidar and Tail Doppler Radar Data over a Hurricane Inner Core for Improved Hurricane Prediction with the NCEP Regional HWRF System

   https://doi.org/10.3390/rs14102367  

   Li, Xin; Pu, Zhaoxia; Zhang, Jun A.; Emmitt, George David (May 2022, Remote Sensing)

   Accurate specification of hurricane inner-core structure is critical to predicting the evolution of a hurricane. However, observations over hurricane inner cores are generally lacking. Previous studies have emphasized Tail Doppler radar (TDR) data assimilation to improve hurricane inner-core representation. Recently, Doppler wind lidar (DWL) has been used as an observing system to sample hurricane inner-core and environmental conditions. The NOAA P3 Hurricane Hunter aircraft has DWL installed and can obtain wind data over a hurricane’s inner core when the aircraft passes through the hurricane. In this study, we examine the impact of assimilating DWL winds and TDR radial winds on the prediction of Hurricane Earl (2016) with the NCEP operational Hurricane Weather Research and Forecasting (HWRF) system. A series of data assimilation experiments are conducted with the Gridpoint Statistical Interpolation (GSI)-based ensemble-3DVAR hybrid system to identify the best way to assimilate TDR and DWL data into the HWRF forecast system. The results show a positive impact of DWL data on hurricane analysis and prediction. Compared with the assimilation of u and v components, assimilation of DWL wind speed provides better hurricane track and intensity forecasts. Proper choices of data thinning distances (e.g., 5 km horizontal thinning and 70 hPa vertical thinning for DWL) can help achieve better analysis in terms of hurricane vortex representation and forecasts. In the analysis and forecast cycles, the combined TDR and DWL assimilation (DWL wind speed and TDR radial wind, along with other conventional data, e.g., NCEP Automated Data Processing (ADP) data) offsets the downgrade analysis from the absence of DWL observations in an analysis cycle and outperforms assimilation of a single type of data (either TDR or DWL) and leads to improved forecasts of hurricane track, intensity, and structure. Overall, assimilation of DWL observations has been beneficial for analysis and forecasts in most cases. The outcomes from this study demonstrate the great potential of including DWL wind profiles in the operational HWRF system for hurricane forecast improvement. 

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2. Time Domain Reflectometry Waveform Interpretation With Convolutional Neural Networks

   https://doi.org/10.1029/2022WR033895  

   Wang, Zhuangji; Hua, Shan; Timlin, Dennis; Kojima, Yuki; Lu, Songtao; Sun, Wenguang; Fleisher, David; Horton, Robert; Reddy, Vangimalla_R; Tully, Katherine (February 2023, Water Resources Research)

   Abstract Interpreting time domain reflectometry (TDR) waveforms obtained in soils with non‐uniform water content is an open question. We design a new TDR waveform interpretation model based on convolutional neural networks (CNNs) that can reveal the spatial variations of soil relative permittivity and water content along a TDR sensor. The proposed model, namely TDR‐CNN, is constructed with three modules. First, the geometrical features of the TDR waveforms are extracted with a simplified version of VGG16 network. Second, the reflection positions in a TDR waveform are traced using a 1D version of the region proposal network. Finally, the soil relative permittivity values are estimated via a CNN regression network. The three modules are developed in Python using Google TensorFlow and Keras API, and then stacked together to formulate the TDR‐CNN architecture. Each module is trained separately, and data transfer among the modules can be facilitated automatically. TDR‐CNN is evaluated using simulated TDR waveforms with varying relative permittivity but under a relatively stable soil electrical conductivity, and the accuracy and stability of the TDR‐CNN are shown. TDR measurements from a water infiltration study provide an application for TDR‐CNN and a comparison between TDR‐CNN and an inverse model. The proposed TDR‐CNN model is simple to implement, and modules in TDR‐CNN can be updated or fine‐tuned individually with new data sets. In conclusion, TDR‐CNN presents a model architecture that can be used to interpret TDR waveforms obtained in soil with a heterogeneous water content distribution. 

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3. Modeling and Monitoring of Wood Moisture Content Using Time-Domain Reflectometry

   https://doi.org/10.3390/f11040479  

   Dahlen, Joseph; Schimleck, Laurence; Schilling, Erik (April 2020, Forests)

   null (Ed.)

   Time-domain reflectometry (TDR) can monitor the moisture content (MC) of water saturated logs stored in wet-decks where the MC exceeds the range that can be measured using traditional moisture meters (>50%). For this application to become routine, it is required that TDR monitoring of wet-decks occurs after establishment, and tools are needed that automate data collection and analysis. We developed models that predict wood MC using three-rod epoxy encased TDR probes inserted into the transverse surface of bolts (prior wet-deck studies were installed on the tangential surface). Models were developed for southern pine, sweetgum, yellow poplar, hickory, red oak, and white oak using a Campbell Scientific TDR100. For each species, at least 37 bolts were soaked for a minimum of three months and then air dried with TDR waveforms, and MC was periodically recorded. Calibrations were developed between MC and the TDR signal using nonlinear mixed effects models. Fixed effects ranged from excellent (southern pine R2 = 0.93) to poor (red oak R2 = 0.36, hickory R2 = 0.38). Independent of wood species, random effects all had a R2 greater than 0.80, which indicates that TDR detects changes in MC at the individual sample level. Use of TDR combined with a datalogger was demonstrated in an operational wet-deck that monitored changes in MC over 12 months, and in a laboratory trial where bolts were exposed to successive wet-dry cycles over 400 days. Both applications demonstrated the utility of TDR to monitor changes in wood MC in high MC environments where periodic measurement is not feasible due to operational safety concerns. Because a saturated TDR reading indicates a saturated MC, and because of the relatively accurate random effects found here, developing individual species models is not necessary for monitoring purposes. Therefore, application of TDR monitoring can be broadly applied for wet-decks, regardless of the species stored. 

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4. Monitoring dynamic water content, bulk density, and heat and water fluxes in the critical zone with thermo‐time domain reflectometry sensors

   https://doi.org/10.1002/vzj2.20390  

   Lu, Yili; Liu, Yutong; Peng, Wei; Fu, Yongwei; Ren, Tusheng; Heitman, Joshua; Horton, Robert (December 2024, Vadose Zone Journal)

   Abstract A thermo‐time domain reflectometry (thermo‐TDR) sensor combines a heat‐pulse sensor with a TDR waveguide to simultaneously measure coupled processes of water, heat, and solute transfer. The sensor can provide repeated in situ measurements of several soil state properties (temperature, soil water content, and ice content), thermal properties (thermal diffusivity, thermal conductivity, heat capacity), and electromagnetic properties (dielectric constant and bulk electrical conductivity) with minimal soil disturbance. Combined with physical or empirical models, structural indicators, such as bulk density and air‐filled porosity, can be derived from measured soil thermal and electrical properties. Successful applications are available to determine fine‐scale heat, water, and vapor fluxes with thermo‐TDR sensors. Applications of thermo‐TDR sensors in complicated scenarios, such as heterogeneous root zones and saline environments, are also possible. Therefore, the multi‐functional uses of thermo‐TDR sensors are invaluable for in situ observations of several soil physical properties and processes in critical zone soils. 

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5. Spatiotemporal Evolution of the Microphysical and Thermodynamic Characteristics of the 20 June 2015 PECAN MCS

   https://doi.org/10.1175/MWR-D-19-0293.1  

   Stechman, Daniel M.; McFarquhar, Greg M.; Rauber, Robert M.; Bell, Michael M.; Jewett, Brian F.; Martinez, Jonathan (April 2020, Monthly Weather Review)

   This study examines microphysical and thermodynamic characteristics of the 20 June 2015 mesoscale convective system (MCS) observed during the Plains Elevated Convection At Night (PECAN) experiment, specifically within the transition zone (TZ), enhanced stratiform rain region (ESR), anvil region, melting layer (ML), and the rear inflow jet (RIJ). Analyses are developed from airborne optical array probe data and multiple-Doppler wind and reflectivity syntheses using data from the airborne NOAA Tail Doppler Radar (TDR) and ground-based Weather Surveillance Radar-1988 Doppler (WSR-88D) radars. Seven spiral ascents/descents of the NOAA P-3 aircraft were executed within various regions of the 20 June MCS. Aggregation modified by sublimation was observed in each MCS region, regardless of whether the sampling was within the RIJ. Sustained sublimation and evaporation of precipitation in subsaturated layers led to a trend of downward moistening across the ESR spirals, with greater degrees of subsaturation maintained when in the vicinity of the descending RIJ. In all cases where melting was observed, the ML acted as a prominent thermodynamic boundary, with differing rates of change in temperature and relative humidity above and below the ML. Two spiral profiles coincident with the rear inflow notch provided unique observations within the TZ and were interpreted in the context of similar observations from the 29 June 2003 Bow Echo and Mesoscale Convective Vortex Experiment MCS. There, sublimation cooling and enhanced descent within the RIJ allowed ice particles to survive to temperatures as warm as +6.8°C before completely sublimating/evaporating. 

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