Turbulence parameters measured by the Beijing Mesosphere–Stratosphere–Troposphere radar in the troposphere/lower stratosphere with three models: Comparison and analyses

Based on the quality-controlled observational spectral width data of the Beijing Mesosphere–Stratosphere–Troposphere (MST) radar in the altitudinal range of 3–19.8 km from 2012 to 2014, this paper analyzes the relationship between the proportion of negative turbulent kinetic energy (N-TKE) and the horizontal wind speed/horizontal wind vertical shear domain, and gives the distributional characteristics of atmospheric turbulence parameters obtained by using different calculation models. Three calculation models of the spectral width method were used in this study—namely, the H model (Hocking, 1985), N-2D model (Nastrom, 1997) and D-H model (Dehghan and Hocking, 2011). The results showed that the proportion of N-TKE in the H model increases with the horizontal wind speed and/or the vertical shear of horizontal wind speed, up to 80 %. When the horizontal wind speed is greater than 40 m·s−1, the proportion of N-TKE in the H model is greater than 60 %, and thus the H model is not applicable. When the horizontal wind speed is greater than 20 m s−1, the proportion of N-TKE in the N-2D model and D-H model increases with the horizontal wind speed, independent of the vertical shear of the horizontal wind speed, and the maximum values are 2 % and 4 %, respectively. However, it is still necessary to consider the applicability of the N-2D model and D-H model in some weather processes with strong winds. The distributional characteristics with height of the turbulent kinetic energy dissipation rate 𝜀 and the vertical eddy diffusion coefficient Kz derived by the three models are consistent with previous studies. Still, there are differences in the values of turbulence parameters. Also, the range resolution of the radar has little effect on the differences in the range of turbulence parameters' values. The median values of 𝜀 in the H model, N-2D model and D-H model are 10−3.2–10−2.8 m2 s−3, 10−2.8–10−2.4 m2 s−3 and 10−3.0–10−2.5 m2 s−3, respectively. The median values of Kz in these three models are 100.18–100.67 m2 s−1, 100.57–100.90 m2 s−1 and 100.44–100.74 m2 s−1.

The aerodynamic performance of Non-planar hex-rotors aircraft subjected the horizontal wind disturbanceLei yao1,2* Ma Chensong1 Feng Zhicheng1

The non-planar hex-rotor aircraft mentioned in this article can change its flight status by simply changing the tilt angle of the rotor. In this paper, mainly studied the best aerodynamic performance of a non-planar hex-rotor aircraft under the influence of horizontal wind (0m/s, 2.5m/s and 4m/s). Firstly, the rotation speed of the rotor is a fixed value (2200r/min) in the low-speed wind tunnel test, the horizontal wind speed and the tilt angle of the rotor are variable values, the thrust, power consumption and power loading(PL) values of the aircraft are obtained. Secondly, the computational fluid dynamics(CFD) method is used to simulate the aerodynamic performance of a non-planar hex-rotors aircraft when subjected to a horizontal wind to obtain the simulation results. Finally, comparing the experimental values and the simulation values, it is found that the horizontal speed has a greater impact on the thrust and power consumption of the non-planar hex-rotor aircraft. From the change of PL values, it is concluded that the horizontal wind speed is 0m/s, 2.5m/s, 4m/s, the best inclination angle is 10°, 30°and 50°, and the strongest anti-wind performance.

Hydrological and Meteorological Controls on Large Wildfire Ignition and Burned Area in Northern California during 2017–2020

This study examined the hydrological/meteorological controls on large wildfires > 10,000 acres (40.5 km2) during 2017–2020 in Northern California at spatial and temporal scales of the target wildfires’ occurrence or growth. This study used the following simple indices for analysis: Moisture Deficit Index (MDI) computed by dividing vapor pressure deficit by soil moisture, MDIWIND computed by multiplying MDI by horizontal wind speed, and MDIGUST computed by multiplying MDI by wind gust speed. The ignition location MDIWIND and MDIGUST showed larger values on the ignition date in fire-years compared to non-fire-years for most of the target wildfires (95.8% and 91.7%, respectively). The peak timing of MDIGUST, which is to evaluate the integrated effect of dry atmosphere/soil and windy condition, coincided with the ignition date for August Complex Fire 2020, Ranch Fire 2018, Claremont-Bear Fire 2020, and Camp Fire 2018. We also found that August Complex Fire 2020, Claremont-Bear Fire 2020, and Camp Fire 2018 occurred in the areas where MDIGUST became spatially and temporally high. Further, strong relationships were found between burned area sizes of the target wildfires and MDI (R = 0.62, p = 0.002), MDIWIND (R = 0.72, p < 0.001), and MDIGUST (R = 0.68, p < 0.001). Overall, the findings in this study implied the strong effect of dry atmosphere/soil and windy conditions on recent large wildfire activities in Northern California. The findings could contribute to a more temporally and spatially detailed forecast of wildfire risks or a better understanding of wildfires’ occurrence and growth mechanisms.

Parameterization of Sea Surface Drag Coefficient for All Wind Regimes Using 11 Aircraft Eddy-Covariance Measurement Databases

The drag coefficient is essential for calculating the aerodynamic friction between air and sea. In this study, we regress a set of relationships between the drag coefficient and the wind speed for different wind ranges using an observational dataset that consists of 5941 estimates of the mean flow and fluxes from 11 aircraft turbulent measurements over the sea surface. Results show that: (1) the drag coefficient is a power function of wind speed over smooth sea surface when it is no greater than 4.5 ms−1, and the drag coefficient decreases with the increase of wind speed; and (2) for rough sea surface, when the wind speed is greater than 4.5 ms−1 and less than or equal to 10.5 ms−1, the drag coefficient increases linearly with the increase of horizontal wind speed; when the wind speed is greater than 10.5 ms−1 and less than or equal to 33.5 ms−1, the drag coefficient changes parabolically with the increase of wind speed; when the wind speed is greater than 33.5 ms−1, the drag coefficient is constant. Additionally, regressed from drag coefficient, the saturated wind speed threshold is 23 ms−1. Parameterizations of turbulent heat transfer coefficient (Ch) and water vapor transfer coefficient (Ce) are also investigated.

Effects of Fences and Green Zones on the Air Flow and PM2.5 Concentration around a School in a Building-Congested District

We investigated the effects of wall- and tree-type fences on the airflow and fine particular matter (PM2.5) concentration around a school using a computational fluid dynamics (CFD) model. First, we validated the simulated wind speeds and PM2.5 concentrations against measured values, and the results satisfied the recommended criteria of the statistical validation indices used. Then, we evaluated the fence effects for 16 inflow directions by conducting numerical simulations with different fence types and heights. With east–southeasterly inflow, relatively high PM2.5 from the road was transported to the school. However, the wall-type fence prevented the PM2.5 from the road from entering the school, and the PM2.5 concentration decreased significantly downwind of the fence. With east–northeasterly inflow, the horizontal wind speed decreased due to the drag caused by the tree-type fence, resulting in a shift in the flow convergence region. The PM2.5 concentration decreased in the region of strengthened upward flow. This occurred because the number of pollutants transported from the background decreased. A comparison of the two fence types revealed that the effect of the tree-type fence on inbound pollutants was more significant, due to increased upward flows, than the effect of the wall-type fence.

Intercomparisons of Tropospheric Wind Velocities Measured by Multi-Frequency Wind Profilers and Rawinsonde

Concurrent measurements of three-dimensional wind velocities made with three co-located wind profilers operated at frequencies of 52 MHz, 449 MHz, and 1.29 GHz for the period 12–16 September 2017 are compared for the first time in this study. The velocity–azimuth display (VAD) method is employed to estimate the wind velocities. The result shows that, in the absence of precipitation, the root mean square difference (RMSD) in the horizontal wind speed velocities U and wind directions D between different pairs of wind profilers are, respectively, in the range of 0.94–0.99 ms−1 and 7.7–8.3°, and those of zonal wind component u and meridional wind component v are in the respective ranges of 0.91–1.02 ms−1 and 1.1–1.24 ms−1. However, the RMSDs between wind profilers and rawinsonde are in the range of 2.89–3.26 ms−1 for horizontal wind speed velocity and 11.17–14.48° for the wind direction, which are around 2–3 factors greater than those between the wind profilers on average. In addition to the RMSDs, MDs between wind profilers and radiosonde are around one order of magnitude larger than those between wind profilers. These results show that the RMSDs, MDs, and Stdds between radars are highly consistent with each other, and they are much smaller than those between radar and rawinsonde. This therefore suggests that the wind profiler-measured horizontal wind velocities are much more reliable, precise, and accurate than the rawinsonde measurement.

Towards All-sky Assimilation of Microwave Temperature Sounding Channels in Environment Canada’s Global Deterministic Weather Prediction System

The all-sky assimilation of radiances from microwave instruments is developed in the 4D-EnVar analysis system at Environment and Climate Change Canada (ECCC). Assimilation of cloud-affected radiances from Advanced Microwave Sounding Unit A (AMSUA) temperature sounding channels 4 and 5 for non-precipitating scenes over the ocean surface is the focus of this study. Cloud-affected radiances are discarded in the ECCC operational data assimilation system due to the limitations of forecast model physics, radiative transfer models, and the strong non-linearity of the observation operator. In addition to using symmetric estimate of innovation standard deviation for quality control, a state-dependent observation error inflation is employed at the analysis stage. The background state clouds are scaled by a factor of 0.5 to compensate for a systematic overestimation by the forecast model, before being used in the observation operator. The changes in the fit of the background state to observations show mixed results. The number of AMSUA channels 4 and 5 assimilated observations in the all-sky experiment is 5-12% higher than in the operational system. The all-sky approach improves temperature analysis when verified against ECMWF operational analysis in the areas where the extra cloud-affected observations were assimilated. Statistically significant reductions in error standard deviation by 1-4% for the analysis and forecasts of temperature, specific humidity, and horizontal wind speed up to maximum 4 days were achieved in the all-sky experiment in the lower troposphere. These improvements result mainly from the use of cloud information for computing the observation-minus-background departures. The operational implementation of all-sky assimilation is planned for Fall 2021.

Doppler wind lidar activities at Cabauw

&lt;p&gt;Atmospheric motion and turbulence are essential parameters for weather and topics related to air quality. Therefore, wind profile measurements play an important role in atmospheric research and meteorology. One source of wind profile data are Doppler wind lidars, which are laser-based remote sensing instruments that measure wind speed and wind direction up to a few hundred meters or even a few kilometers. Commercial wind lidars use the laser wavelength of 1.5 &amp;#181;m and therefore backscatter is mainly from aerosols while clear air backscatter is minimal, limiting the range to the boundary layer typically.&lt;/p&gt;&lt;p&gt;We have carried out a two-year intercomparison of the ZephIR 300M (ZX Lidars) short-range wind lidar and tall mast wind measurements at Cabauw [1]. We have focused on the (height-dependent) data availability of the wind lidar under various meteorological conditions and the data quality through a comparison with in situ wind measurements at several levels in the 213m tall meteorological mast. We have found an overall availability of quality-controlled wind lidar data of 97% to 98 %, where the missing part is mainly due to precipitation events exceeding 1 mm/h or fog or low clouds below 100 m. The mean bias in the horizontal wind speed is within 0.1 m/s with a high correlation between the mast and wind lidar measurements, although under some specific conditions (very high wind speed, fog or low clouds) larger deviations are observed. This instrument is being deployed within North Sea wind farms.&lt;/p&gt;&lt;p&gt;Recently, a scanning long-range wind lidar Windcube 200S (Leosphere/Vaisala) has been installed at Cabauw, as part of the Ruisdael Observatory program [2]. The scanning Doppler wind lidars will provide detailed measurements of the wind field, aerosols and clouds around the Cabauw site, in coordination with other instruments, such as the cloud radar.&lt;/p&gt;&lt;p&gt;[1] Knoop, S., Bosveld, F. C., de Haij, M. J., and Apituley, A.: A 2-year intercomparison of continuous-wave focusing wind lidar and tall mast wind measurements at Cabauw, Atmos. Meas. Tech., 14, 2219&amp;#8211;2235, 2021&lt;/p&gt;&lt;p&gt;[2] https://ruisdael-observatory.nl/&lt;/p&gt;