Lidar Data Assimilation Research Articles

Impact of Lidar Data Assimilation on Simulating Afternoon Thunderstorms near Pingtung Airport, Taiwan: A Case Study

This study focused on improving the forecasting of the afternoon thunderstorm (AT) event on 5 August 2018 near Pingtung Airport in southern Taiwan through a three-dimensional variational data assimilation system using Doppler lidar-based wind profiler data from the Weather and Research Forecast model. The assimilation of lidar wind profiler data had a positive impact on predicting the occurrence and development of ATs and wind fields associated with the local circulations of the sea–land breeze and the mountains. Evaluation of the model quantitative precipitation forecast by using root-mean-square error analysis, Pearson product–moment correlation coefficient analysis, Spearman rank correlation coefficient analysis, and threat and bias scores revealed that experiments using data assimilation performed much better than those not using data assimilation. Among the experiments using data assimilation, when the implementation time of assimilation of the wind profiler data in the model was closer to the occurrence time of the observed ATs, the forecast performance greatly improved. Overall, our assimilation strategy has crucial implications for the prediction of short-duration intense rainfall caused by ATs with small temporal and spatial scales of few hours and a few tens of kilometers. Our strategy can help guarantee the flight safety of aircraft.

An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application

Abstract. Aerosol vertical stratification is important for global climate and planetary boundary layer (PBL) stability, and no single method can obtain spatiotemporally continuous vertical profiles. This paper develops an online data assimilation (DA) framework for the Eulerian atmospheric chemistry-transport model (CTM) Nested Air Quality Prediction Model System (NAQPMS) with the Parallel Data Assimilation Framework (PDAF) as the NAQPMS-PDAF for the first time. Online coupling occurs based on a memory-based way with two-level parallelization, and the arrangement of state vectors during the filter is specifically designed. Scaling tests demonstrate that the NAQPMS-PDAF can make efficient use of parallel computational resources for up to 25 000 processors with a weak scaling efficiency of up to 0.7. The 1-month long aerosol extinction coefficient profiles measured by the ground-based lidar and the concurrent hourly surface PM2.5 are solely and simultaneously assimilated to investigate the performance and application of the DA system. The hourly analysis and subsequent 1 h simulation are validated through lidar and surface PM2.5 measurements assimilated and not assimilated. The results show that lidar DA can significantly improve the underestimation of aerosol loading, especially at a height of approximately 400 m in the free-running (FR) experiment, with the mean bias (BIAS) changing from −0.20 (−0.14) km−1 to −0.02 (−0.01) km−1 and correlation coefficients increasing from 0.33 (0.28) to 0.91 (0.53) averaged over sites with measurements assimilated (not assimilated). Compared with the FR experiment, simultaneously assimilating PM2.5 and lidar can have a more consistent pattern of aerosol vertical profiles with a combination of surface PM2.5 and lidar, independent extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET). Lidar DA has a larger temporal impact than that in PM2.5 DA but has deficiencies in subsequent quantification on the surface PM2.5. The proposed NAQPMS-PDAF has great potential for further research on the impact of aerosol vertical distribution.

Impact of lidar data assimilation on planetary boundary layer wind and PM2.5 prediction in Taiwan

Accurate simulations of planetary boundary layer (PBL) processes in numerical weather models are vital for air quality predictions. Advanced remote sensing techniques, such as lidar detection and ranging (lidar), can provide aerosol information with high temporal and vertical resolutions in the PBL. In this study, a lidar data assimilation system was developed based on the Weather Research and Forecasting-Local Ensemble Transform Kalman Filter (WRF-LETKF) framework coupled with the Community Multiscale Air Quality (CMAQ) model. The objective was to investigate the impact of lidar data assimilation on PBL prediction and the subsequent influence on PM2.5 prediction for a high-air-pollution event.The fine particulate matter (PM2.5) profiles retrieved from two micropulse lidar observations in northern and central Taiwan were assimilated in the WRF-LETKF system. Three numerical experiments, BASE (with a nudging strategy), CTRL (with an ensemble framework), and LDA (with assimilation of lidar-retrieved PM2.5 profiles), were conducted for a high-air-pollution episode. The BASE simulation overestimates the wind speed, which also leads to PM2.5 underestimation. The CTRL and LDA simulations are able to improve the wind fields and enhance the PM2.5 accumulation. With a strong error correlation between the lidar-retrieved PM2.5 concentration and the wind fields, the LDA simulation effectively corrects the wind flow from the surface to the PBL top, which further adjusts the PM2.5 transport processes and leads to results that agree well with observations.

Three-dimensional variational assimilation of Lidar extinction profiles: Application to PM2.5 prediction in north China

The capability of assimilating aerosol optical properties (AOP) using the three-dimensional variational (3DVAR) data assimilation (DA) approach is developed for the Weather Research and Forecasting (WRF) model's data assimilation (WRFDA) system by introducing WRF-Chem-based AOP observation operator and its tangent linear and adjoint. The new AOP DA capability of WRFDA- Chem is firstly applied to assimilate Lidar extinction coefficient profiles in Beijing City from 26 February to March 5, 2019. Cycling strategy of Lidar DA is important to allow a substantial positive impact on surface PM2.5 forecast over the whole evaluation domain (D02, 100 times area of Beijing city covered by the eight Lidar sites) for the entire 24-h forecast range. Compared to the control run without aerosol DA, the mean RMSE reduction and correlation coefficient increase of surface PM2.5 forecast can be up to 15% for the one-day partial cycling strategy. Lidar DA exhibits a clear benefit in improving the vertical distribution of aerosols over Beijing when comparing to surface PM2.5 DA. With the very limited coverage of Lidar data, however, adding Lidar data above surface PM2.5 data (covering the whole D02) does not show the further improvement of surface PM2.5 forecast over D02.

Predictive Capability of WRF Cycling 3DVAR: LiDAR Assimilation at FINO1

In this study, we assess tentatively the predictive capability of the Weather Research and Forecasting (WRF) model and its Three-Dimensional Variational (3DVAR) system. We study the impact of LiDAR data assimilation on predictions of wind speed and direction. The simulation domain covers the German wind energy research platform FINO1 in the Southern North Sea, at which the LiDAR data were recorded. The results demonstrate the significance of applying the LiDAR Data Assimilation (DA) with a short assimilation interval (1-hour) to improve the wind prediction and the temporal and spatial representation of low level jet events, compared with an experiment without DA. Furthermore, we briefly examine the impacts of data assimilation cycle length on predictive performance of the DA system, and spatial variability of wind speed over the study area at a height of 250 m.

A physics perspective on lidar data assimilation for mobile robots

AbstractThis paper presents a new algorithm for lidar data assimilation relying on a new forward model. Current mapping algorithms suffer from multiple shortcomings, which can be related to the lack of clear forward model. In order to address these issues, we provide a mathematical framework where we show how the use of coarse model parameters results in a new data assimilation problem. Understanding this new problem proves essential to derive sound inference algorithms. We introduce a model parameter specifically tailored for lidar data assimilation, which closely relates to the local mean free path. Using this new model parameter, we derive its associated forward model and we provide the resulting mapping algorithm. We further discuss how our proposed algorithm relates to usual occupancy grid mapping. Finally, we present an example with real lidar measurements.

Impact of Lidar Data Assimilation on Low-Level Wind Shear Simulation at Lanzhou Zhongchuan International Airport, China: A Case Study

Doppler wind lidar has played an important role in alerting low-level wind shear (LLW). However, these high-resolution observations are underused in the model-based analysis and forecasting of LLW. In this regard, we employed the Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-VAR) system to investigate the impact of lidar data assimilation (DA) on LLW simulations. Eight experiments (including six assimilation experiments) were designed for an LLW process as reported by pilots, in which different assimilation intervals, assimilation timespans, and model vertical resolutions were examined. Verified against observations from Doppler wind lidar and an automated weather observing system (AWOS), the introduction of lidar data is helpful for describing the LLW event, which can represent the temporal and spatial features of LLW, whereas experiments without lidar DA have no ability to capture LLW. While lidar DA has an obviously positive role in simulating LLW in the 10–20 min after the assimilation time, this advantage cannot be maintained over a longer time. Therefore, a smaller assimilation interval is favorable for improving the simulated effect of LLW. In addition, increasing the vertical resolution does not evidently improve the experimental results, either with or without assimilation.

Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing

A three-dimensional variational (3DVAR) lidar data assimilation method is developed based on the Community Radiative Transfer Model (CRTM) and Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. A 3DVAR data assimilation (DA) system using lidar extinction coefficient observation data is established, and variables from the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) mechanism of the WRF-Chem model are employed. Hourly lidar extinction coefficient data from 12:00 to 18:00 UTC on March 13, 2018 at four stations in Beijing are assimilated into the initial field of the WRF-Chem model; subsequently, a 24 h PM2.5 concentration forecast is made. Results indicate that assimilating lidar data can effectively improve the subsequent forecast. PM2.5 forecasts without using lidar DA are remarkably underestimated, particularly during heavy haze periods; in contrast, forecasts of PM2.5 concentrations with lidar DA are closer to observations, the model low bias is evidently reduced, and the vertical distribution of the PM2.5 concentration in Beijing is distinctly improved from the surface to 1200 m. Of the five aerosol species, improvements of NO3− are the most significant. The correlation coefficient between PM2.5 concentration forecasts with lidar DA and observations at 12 stations in Beijing is increased by 0.45, and the corresponding average RMSE is decreased by 25 μg·m−3, which respectively compared to those without DA.

Impact of Different Satellite Wind Lidar Telescope Configurations on NCEP GFS Forecast Skill in Observing System Simulation Experiments

AbstractThe Global Wind Observing Sounder (GWOS) concept, which has been developed as a hypothetical space-based hybrid wind lidar system by NASA in response to the 2007 National Research Council (NRC) decadal survey, is expected to provide global wind profile observations with high vertical resolution, precision, and accuracy when realized. The assimilation of Doppler wind lidar (DWL) observations anticipated from the GWOS is being conducted as a series of observing system simulation experiments (OSSEs) at the Joint Center for Satellite Data Assimilation (JCSDA). A companion paper (Riishøjgaard et al.) describes the simulation of this lidar wind data and evaluates the impact on global numerical weather prediction (NWP) of the baseline GWOS using a four-telescope configuration to provide independent line-of-sight wind speeds, while this paper sets out to assess the NWP impact of GWOS equipped with alternative paired configurations of telescopes. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and the Global Forecast System (GFS) were used, at a resolution of T382 with 64 layers, as the assimilation system and the forecast model, respectively, in these lidar OSSEs. A set of 45-day assimilation and forecast experiments from 2 July to 15 August 2005 was set up and executed.In this OSSE study, a control simulation utilizing all of the data types assimilated in the operational GSI/GFS system was compared to three OSSE simulations that added lidar wind data from the different configurations of telescopes (one-, two-, and four-look configurations). First, the root-mean-square error (RMSE) of wind analysis is compared against the nature run. A significant reduction of the stratospheric RMSE of wind analyses is found for all latitudes when lidar wind profiles are used in the assimilation system. The forecast impacts of lidar data on the wind and mass forecasts are also presented. In addition, the anomaly correlations (AC) of geopotential height forecasts at 500 hPa were evaluated to compare the control and different GWOS telescope configuration experiments. The results show that the assimilation of lidar data from the GWOS (one, two, or four looks) can improve the NCEP GFS wind and mass field forecasts. The addition of the simulated lidar wind observations leads to a statistically significant increase in AC scores.

Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin

Abstract. This paper presents a new application of assimilating lidar signals to aerosol forecasting. It aims at investigating the impact of a ground-based lidar network on the analysis and short-term forecasts of aerosols through a case study in the Mediterranean basin. To do so, we employ a data assimilation (DA) algorithm based on the optimal interpolation method developed in the Polair3D chemistry transport model (CTM) of the Polyphemus air quality modelling platform. We assimilate hourly averaged normalised range-corrected lidar signals (PR2) retrieved from a 72 h period of intensive and continuous measurements performed in July 2012 by ground-based lidar systems of the European Aerosol Research Lidar Network (EARLINET) integrated into the Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS) network and an additional system in Corsica deployed in the framework of the pre-ChArMEx (Chemistry-Aerosol Mediterranean Experiment)/TRAQA (TRAnsport à longue distance et Qualité de l'Air) campaign. This lidar campaign was dedicated to demonstrating the potential operationality of a research network like EARLINET and the potential usefulness of assimilation of lidar signals to aerosol forecasts. Particles with an aerodynamic diameter lower than 2.5 μm (PM2.5) and those with an aerodynamic diameter higher than 2.5 μm but lower than 10 μm (PM10–2.5) are analysed separately using the lidar observations at each DA step. First, we study the spatial and temporal influences of the assimilation of lidar signals on aerosol forecasting. We conduct sensitivity studies on algorithmic parameters, e.g. the horizontal correlation length (Lh) used in the background error covariance matrix (50 km, 100 km or 200 km), the altitudes at which DA is performed (0.75–3.5 km, 1.0–3.5 km or 1.5–3.5 km a.g.l.) and the assimilation period length (12 h or 24 h). We find that DA with Lh = 100 km and assimilation from 1.0 to 3.5 km a.g.l. during a 12 h assimilation period length leads to the best scores for PM10 and PM2.5 during the forecast period with reference to available measurements from surface networks. Secondly, the aerosol simulation results without and with lidar DA using the optimal parameters (Lh = 100 km, an assimilation altitude range from 1.0 to 3.5 km a.g.l. and a 12 h DA period) are evaluated using the level 2.0 (cloud-screened and quality-assured) aerosol optical depth (AOD) data from AERONET, and mass concentration measurements (PM10 or PM2.5) from the French air quality (BDQA) network and the EMEP-Spain/Portugal network. The results show that the simulation with DA leads to better scores than the one without DA for PM2.5, PM10and AOD. Additionally, the comparison of model results to evaluation data indicates that the temporal impact of assimilating lidar signals is longer than 36 h after the assimilation period.

Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign

Abstract. In this study, we investigate the ability of the chemistry transport model (CTM) Polair3D of the air quality modelling platform Polyphemus to simulate lidar backscattered profiles from model aerosol concentration outputs. This investigation is an important preprocessing stage of data assimilation (validation of the observation operator). To do so, simulated lidar signals are compared to hourly lidar observations performed during the MEGAPOLI (Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation) summer experiment in July 2009, when a ground-based mobile lidar was deployed around Paris on-board a van. The comparison is performed for six different measurement days, 1, 4, 16, 21, 26 and 29 July 2009, corresponding to different levels of pollution and different atmospheric conditions. Overall, Polyphemus well reproduces the vertical distribution of lidar signals and their temporal variability, especially for 1, 16, 26 and 29 July 2009. Discrepancies on 4 and 21 July 2009 are due to high-altitude aerosol layers, which are not well modelled. In the second part of this study, two new algorithms for assimilating lidar observations based on the optimal interpolation method are presented. One algorithm analyses PM10 (particulate matter with diameter less than 10 μm) concentrations. Another analyses PM2.5 (particulate matter with diameter less than 2.5 μm) and PM2.5–10 (particulate matter with a diameter higher than 2.5 μm and lower than 10 μm) concentrations separately. The aerosol simulations without and with lidar data assimilation (DA) are evaluated using the Airparif (a regional operational network in charge of air quality survey around the Paris area) database to demonstrate the feasibility and usefulness of assimilating lidar profiles for aerosol forecasts. The evaluation shows that lidar DA is more efficient at correcting PM10 than PM2.5, probably because PM2.5 is better modelled than PM10. Furthermore, the algorithm which analyses both PM2.5and PM2.5–10 provides the best scores for PM10. The averaged root-mean-square error (RMSE) of PM10 is 11.63 μg m−3 with DA (PM2.5 and PM2.5–10), compared to 13.69 μg m−3 with DA (PM10) and 17.74 μg m−3 without DA on 1 July 2009. The averaged RMSE of PM10 is 4.73 μg m−3 with DA (PM2.5 and PM2.5–10), against 6.08 μg m−3 with DA (PM10) and 6.67 μg m−3 without DA on 26 July 2009.

Assimilation of ground versus lidar observations for PM<sub>10</sub> forecasting

Abstract. This article investigates the potential impact of future ground-based lidar networks on analysis and short-term forecasts of particulate matter with a diameter smaller than 10 μm (PM10). To do so, an Observing System Simulation Experiment (OSSE) is built for PM10 data assimilation (DA) using optimal interpolation (OI) over Europe for one month from 15 July to 15 August 2001. First, using a lidar network with 12 stations and representing the "true" atmosphere by a simulation called "nature run", we estimate the efficiency of assimilating the lidar network measurements in improving PM10 concentration for analysis and forecast. It is compared to the efficiency of assimilating concentration measurements from the AirBase ground network, which includes about 500 stations in western Europe. It is found that assimilating the lidar observations decreases by about 54% the root mean square error (RMSE) of PM10 concentrations after 12 h of assimilation and during the first forecast day, against 59% for the assimilation of AirBase measurements. However, the assimilation of lidar observations leads to similar scores as AirBase's during the second forecast day. The RMSE of the second forecast day is improved on average over the summer month by 57% by the lidar DA, against 56% by the AirBase DA. Moreover, the spatial and temporal influence of the assimilation of lidar observations is larger and longer. The results show a potentially powerful impact of the future lidar networks. Secondly, since a lidar is a costly instrument, a sensitivity study on the number and location of required lidars is performed to help define an optimal lidar network for PM10 forecasts. With 12 lidar stations, an efficient network in improving PM10 forecast over Europe is obtained by regularly spacing the lidars. Data assimilation with a lidar network of 26 or 76 stations is compared to DA with the previously-used lidar network. During the first forecast day, the assimilation of 76 lidar stations' measurements leads to a better score (the RMSE decreased by about 65%) than AirBase's (the RMSE decreased by about 59%).

Evaluating the impact of assimilating CALIOP-derived aerosol extinction profiles on a global mass transport model

[1] Coupled two/three-dimensional variational (2D/3DVAR) assimilation of aerosol physical properties retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR) and Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite-borne instruments is described for the U. S. Navy Aerosol Analysis and Prediction System (NAAPS) global aerosol mass transport model. Coupled 2D/3DVAR assimilation for NAAPS is evaluated for 48-hr forecast cycles, computed four times daily in six-hour intervals, versus stand-alone 2DVAR assimilation of MODIS and MISR aerosol optical depths (AOD). Both systems are validated against AERONET ground-based sun photometer measurements of AOD. Despite a narrow nadir viewing swath and more than 2700 km of equatorial separation between orbits, satellite lidar data assimilation elicits a positive model response. Improvements in analysis and forecast AOD absolute errors are found over both land and maritime AERONET sites. The primary impact to the model from 3DVAR assimilation is the redistribution of aerosol mass into the boundary layer, though the process is sensitive to parameterization of vertical error correlation lengths.

The Effects of Snow Cover and Soil Moisture on Asian Dust: II. Emission Estimation by Lidar Data Assimilation

This paper is the second of a series that describes the effects of snow cover and soil moisture on Asian dust during spring. Whereas the first paper in this series discussed the importance of snow cover and soil moisture estimation, here, we focus on the correctness of the dust emission intensity results based on data assimilation under the assumption that simulation models yield errors in snow cover and soil moisture. We utilized global satellite lidar measurements and a four-dimensional ensemble Kalman filter to optimize the dust emission simulation. The data assimilation results were evaluated by a comparison with independent ground-based lidar measurements. The data assimilation procedure resulted in an increase in the dust emission in the Gobi region during the dust event from March 25 to April 3, 2007, and it improved the analysis of dust concentrations in the leeward region. Without data assimilation, the dust concentrations were underestimated owing to the wet surface conditions of the dust source region. This paper confirms that the improvement of snow cover and soil moisture estimation is important in the analysis of Asian dust levels, and it demonstrates that data assimilation is a powerful tool that can contribute to such improvement.