scholarly journals Mobile Ka-band Polarimetric Doppler Radar Observations of Wildfire Smoke Plumes

2021 ◽  
Author(s):  
Taylor B. Aydell ◽  
Craig B. Clements
Keyword(s):  
Radial Velocity ◽  
Doppler Radar ◽  
Radar Reflectivity ◽  
Doppler Spectrum ◽  
Fine Scale ◽  
Prescribed Burn ◽  
Ka Band ◽  
Wildfire Smoke ◽  
Smoke Plumes ◽  
Spectrum Width

AbstractRemote sensing techniques have been used to study and track wildfire smoke plume structure and evolution, however knowledge gaps remain due to the limited availability of observational datasets aimed at understanding fine-scale fire-atmosphere interactions and plume microphysics. While meteorological radars have been used to investigate the evolution of plume rise in time and space, highly resolved plume observations are limited. In this study, we present a new mobile millimeter-wave (Ka-band) Doppler radar system acquired to sample the fine-scale kinematics and microphysical properties of active wildfire smoke plumes from both wildfires and large prescribed fires. Four field deployments were conducted in the fall of 2019 during two wildfires in California and one prescribed burn in Utah. Radar parameters investigated in this study include reflectivity, radial velocity, Doppler spectrum width, Differential Reflectivity (ZDR), and copolarized correlation coefficients (ρHV). Observed radar reflectivity ranged between -15 and 20 dBZ in plume and radial velocity ranged 0 to 16 m s-1. Dual-polarimetric observations revealed that scattering sources within wildfire plumes are primarily nonspherical and oblate shaped targets as indicated by ZDR values measuring above 0 and ρHV values below 0.8 within the plume. Doppler spectrum width maxima were located near the updraft core region and were associated with radar reflectivity maxima.

Assessing the Errors of Cloud Liquid Water and Precipitation Flux Retrievals in Marine Stratocumulus Based on Doppler Radar Parameters

2007 ◽  
Vol 8 (4) ◽  
pp. 665-677 ◽  
Author(s):  
Yefim L. Kogan ◽  
Zena N. Kogan ◽  
David B. Mechem
Keyword(s):  
Liquid Water ◽  
Doppler Radar ◽  
Radar Reflectivity ◽  
Doppler Spectrum ◽  
Doppler Velocity ◽  
Cloud Liquid Water ◽  
Eddy Simulation ◽  
Air Turbulence ◽  
Spectrum Width ◽  
Drop Size Distributions

Abstract The errors of formulations of cloud retrievals based on radar reflectivity, mean Doppler velocity, and Doppler spectrum width are evaluated under the controlled framework of the Observing System Simulation Experiments (OSSEs). Cloud radar parameters are obtained from drop size distributions generated by the high-resolution Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) large-eddy simulation (LES) model with explicit microphysics. It is shown that in drizzling stratocumulus the accuracy of cloud liquid water (Ql) retrieval can be substantially increased when information on Doppler velocity or Doppler spectrum width is included in addition to radar reflectivity. In the moderate drizzle case (drizzle rate R of about 1 mm day−1) the mean and standard deviation of errors is of the order of 10% for Ql values larger than 0.2 g m−3; in stratocumulus with heavy drizzle (R > 2 mm day−1) these values are approximately 20%–30%. Similarly, employing Doppler radar parameters significantly improves the accuracy of drizzle flux retrieval. The use of Doppler spectrum width σd instead of Doppler velocity yields about the same accuracy, thus demonstrating that both Doppler parameters have approximately the same potential for improving microphysical retrievals. It is noted that the error estimates herein represent the theoretical lower bound on retrieval errors, because the actual errors will inevitably increase, first and foremost, due to uncertainties in estimation contributions from air turbulence.

scholarly journals Typhoon rainstorm simulation with radar data assimilation in southeast coast of China

2020 ◽  
Author(s):  
Jiyang Tian ◽  
Ronghua Liu ◽  
Liuqian Ding ◽  
Liang Guo ◽  
Bingyu Zhang
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Doppler Radar ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Time Interval ◽  
Storm Events ◽  
Southeast Coast ◽  
Assimilation Time ◽  
Radar Data Assimilation

Abstract. As an effective technique to improve the rainfall forecast, data assimilation plays an important role in meteorology and hydrology. The aim of this study is to explore the reasonable use of Doppler radar data assimilation to correct the initial and lateral boundary conditions of the Numerical Weather Prediction (NWP) systems. The Weather Research and Forecasting (WRF) model is applied to simulate three typhoon storm events in southeast coast of China. Radar data from Changle Doppler radar station are assimilated with three-dimensional variational data assimilation (3-DVar) model. Nine assimilation modes are designed by three kinds of radar data (radar reflectivity, radial velocity, radar reflectivity and radial velocity) and three assimilation time intervals (1 h, 3 h and 6 h). The rainfall simulations in a medium-scale catchment, Meixi, are evaluated by three indices including relative error (RE), critical success index (CSI) and root mean square error (RMSE). Assimilating radial velocity with time interval of 1 h can significantly improve the rainfall simulations and outperforms the other modes for all the three storm events. Shortening the assimilation time interval can improve the rainfall simulations in most cases, while assimilating radar reflectivity always leads to worse simulation as the time interval shortens. The rainfall simulation can be improved by data assimilation as a whole, especially for the heavy rainfall with strong convection. The findings provide references for improving the typhoon rainfall forecasts in catchment scale and have great significance on typhoon rainstorm warning.

scholarly journals Coupled Contributions in the Doppler Radar Spectrum Width Equation

2008 ◽  
Vol 25 (12) ◽  
pp. 2245-2258 ◽  
Author(s):  
Ming Fang ◽  
Richard J. Doviak
Keyword(s):  
Doppler Radar ◽  
Central Moment ◽  
Doppler Spectrum ◽  
Spectral Broadening ◽  
Theoretical Derivation ◽  
Central Moments ◽  
Spectrum Width

Abstract Contrary to accepted usage, the second central moment of the Doppler spectrum is not the sum of the second central moments of individual spectral broadening mechanisms. A rigorous theoretical derivation of the spectrum width observed with short dwell times reveals that the sum cannot strictly be taken for the variances associated with various spectral broadening mechanisms and that an added-term coupling shear with turbulence is needed. Furthermore, shear and antenna rotation are coupled. The theoretical expressions derived herein apply to radars with fixed or scanning beams.

scholarly journals Doppler Radar Data Assimilation for Mesoscale Numerical Rainfall Prediction

10.29007/h6dv ◽  
2018 ◽  
Author(s):  
Jiyang Tian ◽  
Jia Liu ◽  
Chuanzhe Li ◽  
Fuliang Yu
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Wrf Model ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Rainfall Prediction ◽  
Doppler Radar Data ◽  
Radar Data Assimilation

Hydrological prediction needs high-resolution and accurate rainfall information, which can be provided by mesoscale Numerical Weather Prediction (NWP) models. However, the predicted rainfall is not always satisfactory for hydrological use. The assimilation of Doppler radar observations is found to be an effective method through correcting the initial and lateral boundary conditions of the NWP model. The aim of this study is to explore an efficient way of Doppler radar data assimilation from different height layers for mesoscale numerical rainfall prediction. The Weather Research and Forecasting (WRF) model is applied to the Zijingguan catchment located in semi-humid and semi-arid area of Northern China. Three-dimensional variational data assimilation (3-DVar) technique is adopted to assimilate the Doppler radar data. Radar reflectivity and radial velocity are assimilated separately and jointly. Each type of radar data are divided into seven data sets according to the observation heights: (1) <500m; (2) <1000m; (3) <2000m; (4) 500~1000m; (5) 1000~2000m; (6) >2000m; (7) all heights. Results show that the assimilation of radar reflectivity leads to better results than radial velocity. The accuracy of the predicted rainfall deteriorates as the rise of the observation height of the assimilated radar data. Conclusions of this study provide a reference for efficient utilisation of the Doppler radar data in numerical rainfall prediction for hydrological use.

An assimilation test of Doppler radar reflectivity and radial velocity from different height layers in improving the WRF rainfall forecasts

2017 ◽  
Vol 198 ◽  
pp. 132-144 ◽  
Author(s):  
Jiyang Tian ◽  
Jia Liu ◽  
Denghua Yan ◽  
Chuanzhe Li ◽  
Zhigang Chu ◽  
...  
Keyword(s):  
Radial Velocity ◽  
Doppler Radar ◽  
Radar Reflectivity

Evolution of Peroxyacetyl Nitrate (PAN) in wildfire smoke plumes detected by the Cross-Track Infrared Sounder (CrIS) over the western U.S. during summer 2018.

2021 ◽  
Author(s):  
Julieta F. Juncosa Calahorrano ◽  
Vivienne H. Payne ◽  
Susan Kulawik ◽  
Bonne Ford ◽  
Frank Flocke ◽  
...  
Keyword(s):  
Peroxyacetyl Nitrate ◽  
Wildfire Smoke ◽  
Smoke Plumes ◽  
The Cross ◽  
Cross Track

scholarly journals Precipitation cloud identification based on faster-RCNN for Doppler weather radar

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yuanbo Ran ◽  
Haijiang Wang ◽  
Li Tian ◽  
Jiang Wu ◽  
Xiaohong Li
Keyword(s):  
Radial Velocity ◽  
Vertical Direction ◽  
Weather Radar ◽  
Radar Reflectivity ◽  
Convective Precipitation ◽  
Horizontal Wind ◽  
Doppler Weather Radar ◽  
Velocity Data ◽  
Precipitation Processes ◽  
Radial Velocity Data

AbstractPrecipitation clouds are visible aggregates of hydrometeor in the air that floating in the atmosphere after condensation, which can be divided into stratiform cloud and convective cloud. Different precipitation clouds often accompany different precipitation processes. Accurate identification of precipitation clouds is significant for the prediction of severe precipitation processes. Traditional identification methods mostly depend on the differences of radar reflectivity distribution morphology between stratiform and convective precipitation clouds in three-dimensional space. However, all of them have a common shortcoming that the radial velocity data detected by Doppler Weather Radar has not been applied to the identification of precipitation clouds because it is insensitive to the convective movement in the vertical direction. This paper proposes a new method for precipitation clouds identification based on deep learning algorithm, which is according the distribution morphology of multiple radar data. It mainly includes three parts, which are Constant Altitude Plan Position Indicator data (CAPPI) interpolation for radar reflectivity, Radial projection of the ground horizontal wind field by using radial velocity data, and the precipitation clouds identification based on Faster-RCNN. The testing result shows that the method proposed in this paper performs better than the traditional methods in terms of precision. Moreover, this method boasts great advantages in running time and adaptive ability.

Fine-Scale Fire Spread in Pine Straw

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 69
Author(s):  
Daryn Sagel ◽  
Kevin Speer ◽  
Scott Pokswinski ◽  
Bryan Quaife
Keyword(s):  
Fire Spread ◽  
Small Scale ◽  
Natural Setting ◽  
Fine Scale ◽  
Prescribed Burn ◽  
Fire Dynamics ◽  
Rate Of Spread ◽  
Scale Models ◽  
Fire Front ◽  
Visible Images

Most wildland and prescribed fire spread occurs through ground fuels, and the rate of spread (RoS) in such environments is often summarized with empirical models that assume uniform environmental conditions and produce a unique RoS. On the other hand, representing the effects of local, small-scale variations of fuel and wind experienced in the field is challenging and, for landscape-scale models, impractical. Moreover, the level of uncertainty associated with characterizing RoS and flame dynamics in the presence of turbulent flow demonstrates the need for further understanding of fire dynamics at small scales in realistic settings. This work describes adapted computer vision techniques used to form fine-scale measurements of the spatially and temporally varying RoS in a natural setting. These algorithms are applied to infrared and visible images of a small-scale prescribed burn of a quasi-homogeneous pine needle bed under stationary wind conditions. A large number of distinct fire front displacements are then used statistically to analyze the fire spread. We find that the fine-scale forward RoS is characterized by an exponential distribution, suggesting a model for fire spread as a random process at this scale.

Application of IVAP-Based Observation Operator in Radar Radial Velocity Assimilation: The Case of Typhoon Fitow

2017 ◽  
Vol 145 (10) ◽  
pp. 4187-4203 ◽  
Author(s):  
Feng Chen ◽  
Xudong Liang ◽  
Hao Ma
Keyword(s):  
Spatial Distribution ◽  
Radial Velocity ◽  
Doppler Radar ◽  
Tangential Velocity ◽  
Distribution Characteristics ◽  
Observation Operator ◽  
Spatial Distribution Characteristics ◽  
Tangential Wind ◽  
Analysis System ◽  
Background Fields

An improved Doppler radar radial velocity assimilation observation operator is proposed based on the integrating velocity–azimuth process (IVAP) method. This improved operator can ingest both radial wind and its spatial distribution characteristics to deduce the two components of the mean wind within a given area. With this operator, the system can be used to assimilate information from tangential wind and radial wind. On the other hand, because the improved observation operator is defined within a given area, which can be uniformly chosen in both the observation and analysis coordinate systems, it has a thinning function. The traditional observation operator and the improved observation operator, along with their corresponding data processing modules, were implemented in the community Gridpoint Statistical Interpolation analysis system (GSI) to demonstrate the superiority of the improved operator. The results of single analysis unit experiments revealed that the two operators are comparable when the analysis unit is small. When the analysis unit becomes larger, the analysis results of the improved operator are better than those of the traditional operator because the former can ingest more wind information than the latter. The results of a typhoon case study indicated that both operators effectively ingested radial wind information and produced more reasonable typhoon structures than those in the background fields. The tangential velocity relative to the radar was retrieved by the improved operator through ingesting tangential wind information from the spatial distribution characteristics of radial wind. Because of the improved vortex intensity and structure, obvious improvements were seen in both track and intensity predictions when the improved operator was used.

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