Optimization Study of Low-Altitude Turbulence Intensity Modeling Based On TKE-XGBoost

An algorithm to compute high-resolution atmospheric humidity profiling by synergetic use of microwave radiometer and Wind Profiler Radar (WPR) is illustrated. WPR data are input for the computation of the potential refractivity gradient profiles, and combined with radiometer estimates of potential temperature profiles, order to fully retrieve humidity gradient profiles. The algorithm makes use of recent developments WPR signal processing, computing the zeroth, first, and second moments of WPR Doppler spectra via a fuzzy logic method, which provides quality control of radar data the spectral domain. On the radiometric side, we have used a multichannel microwave radiometer profiler (MWRP) which provides continuous estimates of tropospheric temperature and humidity profiles. Finally, the combined algorithm performances retrieving humidity profiles are tested with simultaneous radiosonde in situ measurements. The empirical sets of WPR and MWRP data were provided by the Atmospheric Radiation Measurement (ARM) Program. The synergy of microwave radiometer and wind profiler measurements shows encouraging results and significantly improves the spatial vertical resolution of atmospheric humidity profiles.

Abstract. Observations of the vertical wind profile in Chongqing, a typical mountainous city in China, are important, but they are sparse and have low resolution. To obtain more wind profile data, this study matched the Aeolus track with ground-based wind observation sites in Chongqing in 2021. Based on the obtained results, verification and quality control studies were conducted on the wind observations of a wind profile radar (WPR) with radiosonde (RS) data, and a comparison of the Aeolus Mie-cloudy and Rayleigh-clear wind products (Aeolus winds measured in cloudy and aerosol-rich atmospheric conditions from Mie-channel-collected data and winds measured in clear-air conditions from Rayleigh-collected data) with WPR data was then performed. The conclusions can be summarized as follows: (1) a clear correlation between the wind observations of WPR and RS was found, with a correlation coefficient (R) of 0.71. Their root mean square deviation increased with height but decreased at heights between 3 and 4 km. (2) After quality control using Gaussian filtering (GF) and empirical orthogonal function construction (EOFc; G=87.23 %) of the WPR data, the R between the WPR and RS reached 0.83 and 0.95, respectively. The vertical distribution showed that GF could better retain the characteristics of WPR wind observations but with limited improvement in decreasing deviations, whereas EOFc performed better in decreasing deviations but considerably modified the original characteristics of the wind field, especially regarding intensive vertical wind shear in strong convective weather processes. (3) In terms of the differences between the Aeolus and WPR data, 56.0 % and 67.8 % deviations were observed within ±5 m s−1 for Rayleigh-clear and Mie-cloudy winds (Aeolus winds measured in cloudy and aerosol-rich atmospheric conditions from Mie-channel-collected data and winds measured in clear-air conditions from Rayleigh-collected data) vs WPR winds, respectively. Vertically, large mean differences of both Rayleigh-clean and Mie-cloudy winds versus WPR winds appeared below 1.5 km, which is attributed to the prevailing quiet and small winds within the boundary layer in Chongqing; in this case the movement of molecules and aerosols is mostly affected by irregular turbulence. Additionally, large mean differences at a height range between 4 and 8 km for Mie-cloudy versus WPR winds may be related to the high content of cloud liquid water in the middle troposphere of Chongqing. (4) The differences in both Rayleigh-clear and Mie-cloudy versus WPR winds had changed. Deviations of 58.9 % and 59.6 % were concentrated within ±5 m s−1 for Rayleigh-clear versus WPR winds with GF and EOFc quality control, respectively. In contrast, 69.1 % and 70.2 % of deviations appeared within ±5 m s−1 for Rayleigh-clear versus WPR and EOFc WPR winds, respectively. These results shed light on the comprehensive applications of multi-source wind profile data in mountainous cities or areas with sparse ground-based wind observations.

The radar wind profiler (RWP) present at the SGP central facility operates at 915 MHz and was reconfigured in early 2011, to collect key sets of measurements for precipitation and boundary layer studies. The RWP is configured to run in two main operating modes: a precipitation (PR) mode with frequent vertical observations and a boundary layer (BL) mode that is similar to what has been traditionally applied to RWPs. To address issues regarding saturation of the radar signal, range resolution and maximum range, the RWP PR mode is set to operate with two different pulse lengths, termed as short pulse (SP) and long pulse (LP). Please refer to the RWP handbook (Coulter, 2012) for further information. Data from the RWP PR-SP and PR-LP modes have been extensively used to study deep precipitating clouds, especially their dynamical structure as the RWP data does not suffer from signal attenuation during these conditions (Giangrande et al., 2013). Tridon et al. (2013) used the data collected during the Mid-latitude Continental Convective Cloud Experiment (MC3E) to improve the estimation of noise floor of the RWP recorded Doppler spectra.

A self-consistent remote sensing physical method to retrieve atmospheric humidity high-resolution profiles by synergetic use of a microwave radiometer profiler (MWRP) and wind profiler radar (WPR) is illustrated. The proposed technique is based on the processing of WPR data for estimating the potential refractivity gradient profiles and their optimal combination with MWRP estimates of potential temperature profiles in order to fully retrieve humidity gradient profiles. The combined algorithm makes use of recent developments in WPR signal processing, computing the zeroth-, first-, and second-order moments of WPR Doppler spectra via a fuzzy logic method, which provides quality control of radar data in the spectral domain. On the other hand, the application of neural network to brightness temperatures, measured by a multichannel MWRP, can provide continuous estimates of tropospheric temperature and humidity profiles. Performance of the combined algorithm in retrieving humidity profiles is compared with simultaneous in situ radiosonde observations (raob’s). The empirical sets of WPR and MWRP data were collected at the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) site. Combined microwave radiometer and wind profiler measurements show encouraging results and significantly improve the spatial vertical resolution of atmospheric humidity profiles. Finally, some of the limitations found in the use of this technique and possible future improvements are also discussed.

The common way to process radar wind profiler (RWP) data by moments estimation of the Fourier power spectrum fails in presence of transient intermittent clutter contributions. Wavelets are especially suitable for detecting and removing transient components because of their high localization in time and frequency domain. We give an overview on the wavelet filtering of contaminated discrete RWP signals and introduce a new technique involving the wavelet packet decomposition and a splitting in progressive and regressive signal components. This technique has been successfully tested on severely real-data sets where classical wavelet routines fail.

Various non-atmospheric signals contaminate radar wind profiler (RWP) data, which produce bias in estimation of moments and wind velocity. Especially, in ultra high frequency (UHF) RWPs, ground clutter severely degrades wind velocity estimation. Furthermore, at higher altitudes, noise dominates the clear air signal. Thus, the important tasks of signal processing in a RWP are (i) to eliminate the clutter signal, (ii) to detect the weak atmospheric signals buried inside the noise and (iii) to improve signal-to-noise ratio. Wavelet analysis is a powerful tool to differentiate the characteristics of the ground clutter and noise from the atmospheric turbulence echo at the time series level. The authors have implemented the signal processing for lower atmospheric wind profiler radar at National Atmospheric Research Laboratory, Gadanki, India, using wavelet transforms. In this study, they present the implementation approach and results. The wavelet-based algorithms use different threshold levels to identify and remove ground clutter and to denoise the data. The obtained results using this method are validated with collocated global positioning system radiosonde data.

Summary Radars of various types have been used in ornithological research for about 70 years. However, the potential of radar wind profiler (RWP) as a tool for biological purposes remains poorly understood. The aim of this study is to assess the suitability of RWP for ornithological research questions. A 1290 MHz RWP at the south‐eastern coast of the Bay of Biscay has been known to exhibit seasonally occurring nocturnal signals attributed to migrating birds. As a first step to verify the origin of these seasonal patterns, historical radar data from 2010 to 2012 were analysed, and both bird patterns and temporal occurrence were identified in RWP data at different levels of the signal processing. A thermal‐imaging (TI) camera in conjunction with moon watching was used as verification systems at the radar site to confirm the ornithological origin of the radar echoes. The simultaneous data on spring migration served as a basis for the identification of biological signatures (qualitative parameters) on time‐series level (raw data) and to derive quantitative migration parameters (flight altitude, migration traffic rates) thereof. Finally, the quantitative measurements of the TI camera and the radar were compared considering meteorological conditions. The approach allowed identifying reproducible criteria based on time series to calculate migration traffic rates and altitudinal flight distribution. General flight directions were only available in the final wind data. In clear weather conditions, the calibration methods coincided well with the wind profiler data. Findings show that wind profiler raw data offer reliable information on migration intensity, flight altitudes and flight directions in a variety of meteorological conditions. The method presented can be applied as a complement to present efforts to use weather radars for large‐scale bird monitoring. Furthermore, it is also interesting for the meteorological community to refine signal‐processing methods.

Wind profiler radar (WPR) is used for all-weather atmospheric wind-field monitoring. However, the reliability of these observations reduces significantly when there is electromagnetic interference echo, generally caused by ground objects, birds, or rain. Therefore, to optimize the data reliability of WPR, we proposed a synthetic data quality control process. The process included the application of a minimum connection method, judgment rule, and median test optimization algorithm for optimizing clutter suppression, spectral peak symmetry detection, and radial speed, respectively. We collected the base data from a radiosonde and multiple radars and conducted an experiment using these data and algorithms. The results indicated that the quality control method: (1) had good adaptability to multiple WPRs both in clear sky and precipitation; (2) was useful for suppressing ground clutter and (3) was superior to those of the manufacturer as a whole. Thus, the data quality control method proposed in this study can improve the accuracy and reliability of WPR products and multiple types of WPR, even when they function under vastly different weather conditions.

Using ground-based remote sensing equipment (wind profile radar and microwave radiometer) data and ground automatic station data, a ground-based remote sensing sounding profile system (FAS) is constructed, which aims to make use of its advantages of high resolution, high accuracy, and low cost to make up for the lack of space–time density in existing conventional sounding layer information. The retrieval results of remote sensing sounding profiles in Beijing from May 2021 to September 2022 were tested and evaluated. The results show that the correlation coefficient between FAS and conventional sounding specific humidity is 0.89, the root–mean–square deviation is 1.53 g/kg, and the evolution trends of different data sources of convective available potential energy (CAPE) and vertical wind shear are synchronous. A case study was conducted to evaluate the effectiveness of 40 severe convective processes in the Beijing Plain area. The results show that, due to the minute-level time resolution of FAS, the retrieved convective parameters could track the evolution trend of the atmospheric state with high timeliness, dynamically describe the configuration of thermodynamic parameters, and indicate the time-varying local convective potential and instability level. Therefore, it has certain short-term forecasting significance for the occurrence time, intensity, and convection type.

This study investigates the vertical structure and temporal evolution of Meiyu precipitating cloud systems over Dongshan, China, utilizing data from an L-band Wind Profiler Radar (WPR) collected during the Intensive Observation Periods (IOPs) of 2001 and 2002. The WPR provides crucial insights into hydrometeor characteristics and vertical air motion by measuring reflectivity, reflectivity-weighted fall speed, and variance of hydrometeor fall speeds. Observations from June 2001 revealed the complex interaction between a Meiyu front and Typhoon Chebi (0102), illustrating how their movements influenced precipitation patterns. Detailed WPR data showed varied vertical rainfall structures, with horizontal mixing of rain mass in the lowest 2 km and a distinct bright band near 4.5 km indicating stratiform precipitation. Doppler vertical velocity measurements largely indicated stratiform structures with downward velocities, though weak updrafts were observed within the Meiyu frontal system. An X-band Doppler Radar comparison showed less precipitation in IOP-2001 compared to IOP-2002, with notable diurnal variations in the latter. A key aspect of this research involved classifying Meiyu precipitating clouds into convective, transition, and stratiform types using a WPR-based algorithm that assesses the presence of a melting layer and turbulence/hydrometeors. Convective regions exhibited strong updrafts and high reflectivity, while stratiform areas showed weaker vertical velocities and a clear bright band. Analysis of occurrence percentages revealed that IOP-2001 was dominated by mixed and stratiform clouds, whereas IOP-2002 had a more balanced distribution of convective and stratiform types. These differences were attributed to variations in atmospheric moisture content (relative humidity, precipitable water) and local environmental conditions (CAPE, CIN). This research highlights the effectiveness of WPR data in understanding Meiyu rainfall mechanisms and their potential for improving cloud-scale and mesoscale numerical models, ultimately aiding in better predictions and flood disaster mitigation efforts in East Asia.

The accuracy of the radial wind velocity measured with a radar wind profiler will depend on turbulent variability and instrumental noise. Radial velocity estimates of a boundary layer wind profiler are compared with those estimated by a Doppler lidar over 2.3 h. The lidar resolution volume was much narrower than the profiler volume, but the samples were well matched in range and time. The wind profiler radial velocity was computed using two common algorithms [profiler online program (POP) and National Center for Atmospheric Research improved moments algorithm (NIMA)]. The squared correlation between radial velocities measured with the two instruments was R2 = 0.99, and the standard deviation of the difference was about σr = 0.20–0.23 m s−1 for radial velocities of greater than 1 m s−1 and σr = 0.16–0.35 m s−1 for radial velocities of less than 1 m s−1. Small radial velocities may be treated differently in radar wind profiler processing because of ground-clutter mitigation strategies. A standard d...

In recent years, wind profiler radar observation technology has developed rapidly, and its detection data has high temporal and spatial resolution. The observing wind field information plays an important role in the small or meso scale weather system analysis and numerical weather prediction. This study aimed to explore the wind profiler radar observing layout over China form the perspective of numerical weather prediction application. A heavy precipitation processe occurring in Jianghuai region on June 16-17, 2015 was studied using Weather Research and Forecasting (WRF) model and Three Dimensional Variational data assimilation (3D-Var) method with Observing System Simulation Experiments (OSSEs). Two sets of sensitive tests — the different station separations test and the different types (i.e. different detection heights) test were designed to explore the influence on numerical weather prediction of different wind profiler radar layout, compared with the control test with no data assimilation. The different station separations test designed uniform distribution wind profiler radars with station separations of approximate 500km, 200km and 100km over China. The different types test designed to assimilate wind profiler radars totally in 3km or 8km detection height and a mixture layout of this two types. Result: Compared with the control test, assimilation wind profiler radar data decreased the wind prediction error at each forecast time, and most of the improvement occurred below 300hPa (about 9km). It also can be seen that assimilation wind profiler radar had more effect on the forecast improvement of the U component wind than that of the V component wind. In the different station separations test, the forecast results of the experiments assimilating wind profiler radar data at layout densities of 100km and 200km were similar, and were superior to the experiment of 500km. In the different types test, the result of the experiment that assimilated wind profiler radar data totally in 8km detection height was closed to the experiment assimilating the mixture layout of 8km and 3km, whose performance were both better than that of totally in 3km. Considering the results above and economic cost comprehensively, the 200km layout density and the mixture layout of 3km and 8km detection height wind profiler radars are suggested in the observation layout design of wind profiler radars over China.

Abstract. The mixing layer height (MLH) is a key parameter for boundary layer studies, including meteorology, air quality, and climate. MLH estimates are inferred from in situ radiosonde measurements or remote sensing observations from instruments like lidar, wind profiling radar, or sodar. Methods used to estimate MLH from radiosonde profiles are also used with atmospheric temperature and humidity profiles retrieved by microwave radiometers (MWR). This paper proposes an alternative approach to estimate MLH from MWR data, based on direct observations (brightness temperatures, Tb) instead of retrieved profiles. To our knowledge, MLH estimates directly from Tb observations have never been attempted before. The method consists of a multivariate linear regression trained with an a priori set of collocated MWR Tb observations (multifrequency and multi-angle) and MLH estimates from a state-of-the-art lidar system. The proposed method was applied to a 7-month data set collected at a typical midlatitude site. Results show that the method is able to follow both the diurnal cycle and the day-to-day variability as suggested by the lidar measurements, and also it can detect low MLH values that are below the full overlap limit (~200 m) of the lidar system used. Statistics of the comparison between MWR- and reference lidar-based MLH retrievals show mean difference within 10 m, root mean square within 340 m, and correlation coefficient higher than 0.77. Monthly mean analysis for daytime MLH from MWR, lidar, and radiosonde shows consistent seasonal variability, peaking at ~1200–1400 m in June and decreasing down to ~600 m in October. Conversely, nighttime monthly mean MLH from all methods are within 300–500 m without any significant seasonal variability. The proposed method provides results that are more consistent with radiosonde estimates than MLH estimates from MWR-retrieved profiles. MLH monthly mean values agree well within 1 standard deviation with the bulk Richardson number method applied at radiosonde profiles at 11:00 and 23:00 UTC. The method described herewith operates continuously and is expected to work with analogous performances for the entire diurnal cycle, except during considerable precipitation, demonstrating new potential for atmospheric observation by ground-based microwave radiometry.

The ground-based profiling observations are essential to improve the understanding of specific weather process. Continuous efforts have been made in China on the ground-based remote sensing vertical profiling system, which consists of five instruments: microwave radiometer, millimeter-wave cloud radar, Global Navigation Satellite System/Meteorology (GNSS/MET), wind profiling radar and aerosol lidar. As part of World Meteorological Organization (WMO) global basic observing network (GBON), this system can provide the detailed profiling information of temperature, water vapor, wind, hydrometeors and aerosols.A wide range of products have been developed not only from each instrument itself but also the synthetic uses of multi-source observations. Cloud radar plays a key role in the identification of hydrometeors, precipitation and snowfall. Microwave radiometer brightness temperatures are used to retrieve the temperature and humidity profiles under the clear and cloudy atmosphere. Lidar can character the aerosols with their extinction coefficients, backscatter coefficients and depolarization ratio, which are useful to identify the particle size to distinguish different air pollution, such as haze and sandstorms. GNSS/MET can provide relatively reliable estimates of the integration of water vapor and its vertical distribution. Wind profiling radar can provide the wind estimates including the valuable vertical velocity of atmospheric motions. Besides, the multi-element observations are also utilized to generate the weather signal warning products, such as the precipitation potential and several kinds of indices. Some of these products, such as the radiometer temperature profiles, have also been assessed using the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) dataset in Xilinhot, China.The system is preferred to deploy at the operational in-situ sounding stations, generating the complementary datasets for the sparse temporal samplings of in-situ sounding observations. Owing to its high temporal and vertical spatial resolution, the relatively complete weather processes can be monitored for further analyses and research, such as the low-level jet stream, advection, precipitation, snowfall, etc. For instance, a heavy snowfall process in Beijing on February 20, 2024 and a hail process in Beijing on April 28, 2023 were well captured by this system.Quality control is another essential part to support the better performances of this system. For example, the co-located sounding profiles are used to evaluate the data quality and equipment stability of microwave radiometer. To support the synthetic applications of the spaceborne and ground-based radiometers, an advanced doubling and adding radiative transfer model based on the discrete ordinate method is developed for integrated satellite-ground forward simulations, to avoid the systematic errors resulting from two different ground-based and spaceborne solvers. It can be used to perform the assessments on brightness temperature observations and analyze the potential connections between the upwelling and downwelling brightness temperature observations from the spaceborne and ground-based radiometers. In the future, the instrument calibration and the synthetic uses of their base products can be priorities, to improve and promote this new profiling system.

<p>In recent years, the use of radar wind profilers (RWP) at airports has grown significantly with the aim of supporting decision makers to maintain the safety of aircraft landings and takeoffs.</p><p>The RWP provide vertical profiles of averaged horizontal wind speed and direction and vertical wind velocity for the entire Atmospheric Boundary Layer (ABL) and beyond. In addition, RWP with Radio-Acoustic Sounding System (RASS) are able to retrieve virtual temperature profiles in the ABL.</p><p>RWP data evaluation is usually based on the so-called Doppler Beam Swinging method (DBS) which assumes homogeneity and stationarity of the wind field. Often, transient eddies violate this homogeneity and stationarity requirement. Hence, incorrect wind profiles can relate to transient eddies and present a problem for the forecast of high-impact weather phenomena in airports. This work intends to provide a method for removing outliers in such profiles based on historical data and other variables related to the Atmospheric Boundary Layer stability profile in the study region.</p><p>For this study, a dataset of almost one year retrieved from a RWP LAP3000 with RASS Extension is used for a wind profile correction algorithm development.</p><p>The algorithm consists of the detection of outliers in the wind profiles based on the thermodynamic structure of the ABL and the generation of the corrected profiles.</p><p>Results show that the algorithm is capable of identifying and correcting unrealistic variations in speed caused by transient eddies. The method can be applied as a complement to the RWP data processing for better data reliability.</p><p> </p><p>Keywords: atmospheric boundary layer; stability profile; wind profile</p>