The least-squares (LS) method is commonly employed to retrieve the 3-D wind field by joint detection with multiple sensors, but it tends to suffer from ill-conditioning and reduced accuracy when the sensors are closely deployed and heterogeneous. This letter proposes a new method, denoted as TSVD-LSBE to address the ill-conditioning and sensor heterogeneity problem. First, the LS algorithm, regulated by the truncated singular value decomposition (TSVD), is utilized to get a preliminary estimation of the wind field. Then, the modified Bayesian estimation (BE), which is also regulated by TSVD and uses the modified LS result as the background wind, is employed to tune the preliminary estimation to a more accurate one. Simulation results demonstrate that the new method can enhance the retrieval accuracy by up to 50% in various wind field detection scenarios, surpassing both the modified-LS method and the local and global variational method (LGVM) based on single sensor measurement.

Limited by the achievable pulse peak power, narrowing the transmitted pulsewidth to improve the spatial resolution would reduce the output power of wind Lidar, thereby decreasing the detection range. To solve this dilemma, a time-division multifrequency wind Lidar (TDMWL) is proposed with a long pulse duration consisting of consecutive multiple subpulses at different frequencies. Different detection ranges can be separated by different downconverted frequencies. The accumulation of multifrequency spectra enlarges the output power, while the width of the subpulse determines the spatial resolution. In the experiment, with a subpulse width of 200 ns, the detection range is increased from 750 to above 1200 m by accumulating six frequencies, proving the potential of TDMWL in measuring longer range without deteriorating the spatial resolution.

It is quite challenging for conventional estimators to extract the radial wind velocities from the raw spectrum data of coherent Doppler wind Lidar (CDWL) at very low signal-to-noise ratios (SNR). This work proposes a new wind velocity estimation method based on the constraints from neighboring wind profiles, which can effectively and robustly extend the reliable detection range of CDWL. Considering the spatial continuity of the wind field, priori information from the velocity probability distributions in previous range bins is utilized to reshape the contaminated spectrum of the current range bin. The influences of different preceding range bins are weighted by their overlapping ratios to the contaminated range bin. Iterations are carried out when computing the variance of the Gaussian-shaped probability, which can preserve the wind field details while removing the parameter dependence in practical applications. The method is verified theoretically in simulated atmosphere echoes as well as experimentally in our self-developed dual-frequency pulsed CDWL system. The first results show that reasonable estimates can be obtained in places far beyond the previous detection boundary provided by conventional estimators.

For the first time, a correlation-free coding technique is demonstrated for improving the detection range of coherent Doppler wind Lidar (CDWL) while roughly maintaining the spatial resolution of single-bit duration. Based on the probing pulses modulated by the improved simplex codes (ISCs), backscattered echoes can be considered as the linear superposition of the Lidar responses, each of which corresponds to different detection locations. Then, the Doppler spectra/wind profile approaching the spatial resolution of a single-bit duration can be recovered after decoding, time delay compensation and accumulation. In contrast to those coding techniques that rely on correlation, the proposed method can be unaffected by the unequal amplitudes of coded bits that are stable between pulses. Theoretically, in simulated atmospheric echoes, it is confirmed that the degraded spatial resolution caused by wide overall emitting pulses can be mitigated while the reliable detection range can be improved. Additionally, experimentally constructed coding CDWLs using 9, 16, 32, and 64-bit ISCs show improvements in detection range that are consistent with the theory.

Highly variable wind fields (HWFs), which usually have drastically changing velocities over time, can seriously impact aviation safety, wind energy assessment, and so on. A recently proposed deep learning method can well predict the wind fields in ordinary cases, but its performance deteriorates when HWFs are involved. In this letter, a nowcasting method taking into account the impact of HWFs is proposed. First, standard deviations (SDs) of the lidar observations within a time interval are used to identify highly variable events. Second, a loss function weighted by the SDs is adopted to train the nowcasting neural network. Additionally, a new dimensionless metric is introduced to quantitatively measure the nowcasting performance with emphasis on HWFs. Experimental results demonstrate that the weighted loss function can efficiently improve the nowcasting performance on HWFs such as the initiation and dissipation processes of wind shear. Compared with the original nowcasting method, the network with the weighted loss function can reduce the nowcasting errors by an improvement of more than 15.2% in terms of the new metric.

To better describe the main features of the complex airflow in the atmospheric boundary layer, a hybrid wind field retrieval method based on machine learning (ML) and data assimilation (DA) is proposed. Based on the joint measurement of lidar andin situmeasurements, a 3-D variational data assimilation (3DVAR) method is used to retrieve the fine-scale wind field. To address the iterative interpolation problem in the traditional DA methods, this article isolates the interpolation from the optimization and uses the regression methods in ML to estimate the interpolated observations on analysis grids. More specifically, the supervised regression and semisupervised regression are, respectively, used for lidar andin situobservations according to their heterogeneity. Simulation and field measurement results indicate that, compared with the traditional DA methods, the proposed method can better estimate both 2-D and 3-D velocities, by an improvement of more than 42.3% on average.

The uniform and linear wind models have been commonly used for wind field retrieval in meteorological community. However, the accuracy and robustness of the retrieval results can be quite unsatisfactory due to the mismatch between these models and the real wind distribution, especially under complex wind conditions. In this article, a nonlinear model based on high-order Taylor expansion is proposed to deal with this limitation, and the combination of ridge regression and decomposition-iteration process (denoted as Ridge-DI method) is further introduced to solve the model with high accuracy and robustness. A case study on simulation and field experiment shows that the proposed method with the third-order Taylor expansion can reduce the mean root-mean-square errors (RMSEs) of the retrieved velocities by more than 16.84% in comparison with traditional methods.