Lidar Water Vapor Research Articles

Water vapor Raman lidar observations from multiple sites in the framework of WaLiNeAs

Abstract. During the Water Vapor Lidar Network Assimilation (WaLiNeAs) campaign, eight lidars specifically designed to measure water vapor mixing ratio (WVMR) profiles were deployed on the western Mediterranean coast. The main objectives were to investigate the water vapor content during case studies of heavy-precipitation events in the coastal western Mediterranean and assess the impact of high spatiotemporal WVMR data on numerical weather prediction forecasts by means of state-of-the-art assimilation techniques. Given the increasing occurrence of extreme events due to climate change, WaLiNeAs is the first program in Europe to provide network-like, simultaneous and continuous water vapor profile measurements over a period of 3–4 months. This paper focuses on the WVMR profiling datasets obtained from three of the lidars run by the French part of the WaLiNeAs team. These three lidars were deployed in the cities of Coursan, Le Grau-du-Roi and Cannes. This measurement setup enabled monitoring of the water vapor content of the lower troposphere over periods of 3 months in fall and winter 2022, with some interruptions, and 4 months in summer 2023. The lidars measured the WVMR profiles from the surface up to approximately 6–10 km at nighttime and 1–2 km during daytime. They had a vertical resolution of 100 m and a time resolution between 15 and 30 min, and they were selected to meet the needs of weather forecasting with an uncertainty lower than 0.4 g kg−1. The paper presents details about the instruments, the experimental strategy and the datasets provided. The final dataset (https://doi.org/10.25326/537; Chazette et al., 2023) is divided into two sub-datasets: the first with a time resolution of 15 min, which contains a total of 26 423 WVMR vertical profiles, and the second with a time resolution of 30 min to improve the signal-to-noise ratio and signal altitude range.

The effects of warm-air intrusions in the high Arctic on cirrus clouds

Abstract. Warm-air intrusions (WAIs) are responsible for the transportation of warm and moist air masses from the mid-latitudes into the high Arctic (> 70° N). In this work, we study cirrus clouds that form during WAI events (WAI cirrus) and during undisturbed Arctic conditions (AC cirrus) and investigate possible differences between the two cloud types based on their macrophysical and optical properties with a focus on relative humidity over ice (RHi). We use airborne measurements from the combined high-spectral-resolution and differential-absorption lidar, WALES, performed during the HALO-(AC)3 campaign. We classify each research flight and the measured clouds as either AC or WAI, based on the ambient conditions, and study the macrophysical, geometrical and optical characteristics for each cirrus group. As our main parameter we choose the relative humidity over ice (RHi), which we calculate RHi by combining the lidar water vapor measurements with model temperatures. Ice formation occurs at certain RHi values depending on the dominant nucleation process taking place. RHi can thus be used as an indication of the nucleation process and the structure of cirrus clouds. We find that during WAI events the Arctic is warmer and moister and WAI cirrus clouds are both geometrically and optically thicker compared to AC cirrus. WAI cirrus clouds and the layer directly surrounding them are more frequently supersaturated, also at high supersaturations over the threshold for homogeneous ice nucleation (HOM). AC cirrus clouds have a supersaturation-dominated cloud top and a subsaturated cloud base. WAI cirrus clouds also have high supersaturations at cloud top but also at cloud base.

Water Vapor Lidar Observation and Data Assimilation for a Moist Low-Level Jet Triggering a Mesoscale Convective System

Abstract We conducted field observations using two water vapor Raman lidars (RLs) in Kyushu, Japan, to clarify the characteristics of a moist low-level jet (MLLJ), which plays a fundamental role in the formation and maintenance of mesoscale convective systems (MCSs). The two RLs observed the inside and outside of an MLLJ, providing moisture to an MCS with local heavy precipitation on 9 July 2021. Our observations revealed that the MLLJ contained large amounts of moisture below the convective mixing layer height of 1.6 km. The large amount of moisture in the MLLJ might be intensified by low-level convergences and/or water vapor buoyancy facilitated by strong horizontal wind. We conducted four data assimilation experiments: CNTL that assimilated Japan Meteorological Agency operational observation data and three other experiments that ingested the lidar-derived vertical moisture profiles as well as the operational observation data. The experiments assimilating lidar-derived vertical moisture profiles caused intensification and southwestward extensions of the low-level convergence zone, resulting in local heavy precipitation at lower latitudes in experiments assimilating lidar-derived moisture profiles than in CNTL. All three experiments ingesting vertical moisture profiles generally produced better 9-h precipitation forecasts than CNTL, implying that the assimilation of vertical moisture profiles could be well suited for numerical weather prediction of local heavy precipitation. Moreover, the experiment assimilating both of the two RL sites’ data reproduced better forecast fields than experiments assimilating a single RL site’s data, implying that data assimilation of vertical moisture profiles at multiple RL sites enables us to improve initial conditions compared to a single RL site. Significance Statement Moist low-level jets (MLLJs) are moisture-rich airflows in the low-level atmosphere that play an important role in developing mesoscale convective systems and local heavy rainfall. To better understand the mechanisms affecting the development of local heavy rainfall events and to improve our ability to forecast them, studying the moisture structures in MLLJs is important. We succeeded in observing an MLLJ in western Japan using water vapor Raman lidars (RLs), which obtained vertical moisture profiles, and revealed details of vertical moisture structures in the MLLJ. We also performed data assimilation experiments to examine the impact of assimilating vertical moisture profiles observed by the RLs. The results showed that the assimilation of the moisture data improved the forecasting of local heavy rainfall.

Profiling the molecular destruction rates of temperature and humidity as well as the turbulent kinetic energy dissipation in the convective boundary layer

Abstract. A simultaneous deployment of Doppler, temperature, and water-vapor lidars is able to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). Horizontal wind profiles and profiles of vertical wind, temperature, and moisture fluctuations are combined, and transversal temporal autocovariance functions (ACFs) are determined for deriving the dissipation and molecular destruction rates. These are fundamental loss terms in the TKE as well as the potential temperature and mixing ratio variance equations. These ACFs are fitted to their theoretical shapes and coefficients in the inertial subrange. Error bars are estimated by a propagation of noise errors. Sophisticated analyses of the ACFs are performed in order to choose the correct range of lags of the fits for fitting their theoretical shapes in the inertial subrange as well as for minimizing systematic errors due to temporal and spatial averaging and micro- and mesoscale circulations. We demonstrate that we achieve very consistent results of the derived profiles of turbulent variables regardless of whether 1 or 10 s time resolutions are used. We also show that the temporal and spatial length scales of the fluctuations in vertical wind, moisture, and potential temperature are similar with a spatial integral scale of ≈160 m at least in the mixed layer (ML). The profiles of the molecular destruction rates show a maximum in the interfacial layer (IL) and reach values of ϵm≃7×10-4 g2 kg−2 s−1 for mixing ratio and ϵθ≃1.6×10-3 K2 s−1 for potential temperature. In contrast, the maximum of the TKE dissipation is reached in the ML and amounts to ≃10-2 m2 s−3. We also demonstrate that the vertical wind ACF coefficient kw∝w′2‾ and the TKE dissipation ϵ∝w′2‾3/2. For the molecular destruction rates, we show that ϵm∝m′2‾w′2‾1/2 and ϵθ∝θ′2‾w′2‾1/2. These equations can be used for parameterizations of ϵ, ϵm, and ϵθ. All noise error bars are derived by error propagation and are small enough to compare the results with previous observations and large-eddy simulations. The results agree well with previous observations but show more detailed structures in the IL. Consequently, the synergy resulting from this new combination of active remote sensors enables the profiling of turbulent variables such as integral scales, variances, TKE dissipation, and the molecular destruction rates as well as deriving relationships between them. The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of large-eddy simulations.

Improvement of Two-Hour-Ahead QPF Using Blending Technique with Spatial Maximum Filter for Tolerating Forecast Displacement Errors and Water Vapor Lidar Assimilation

Improvement of Two-Hour-Ahead QPF Using Blending Technique with Spatial Maximum Filter for Tolerating Forecast Displacement Errors and Water Vapor Lidar Assimilation

A review of atmospheric water vapor lidar calibration methods

AbstractAtmospheric water vapor is a crucial factor in the Earth's water cycle. As an important greenhouse gas, changes in the spatio‐temporal distribution of atmospheric water vapor can contribute to the occurrence of various extreme weather phenomena. Lidar, with its high spatial and temporal resolutions, has great potential for applications in water vapor profile detection. Raman lidar and differential absorption lidar (DIAL) have been successfully used to detect atmospheric water vapor. System calibration is crucial to ensure that the measured profile accurately represents the concentration profile of atmospheric water vapor. Choosing an effective system calibration method can ensure the accuracy of long‐term lidar measurements. This paper reviews the latest progress and applications of atmospheric water vapor lidar calibration in recent years. The basic principles of Raman lidar and DIAL calibration are introduced. Various methods and benefits of system calibration are discussed. Raman lidar has three commonly used calibration methods: external calibration, internal calibration, and hybrid calibration methods. The most commonly used method is external calibration based on radiosondes. DIAL is usually implemented with an advantageous self‐calibration method. Finally, potential development directions for atmospheric water vapor lidar and calibration technology are discussed.This article is categorized under: Science of Water > Methods Engineering Water > Methods Human Water > Value of Water

Characteristics of supersaturation in midlatitude cirrus clouds and their adjacent cloud-free air

Abstract. Water vapor measurements of midlatitude cirrus clouds, obtained by the WAter vapour Lidar Experiment in Space (WALES) lidar system during the Mid-Latitude Cirrus (ML-CIRRUS) airborne campaign, which took place in the spring of 2014 over central Europe and the NE Atlantic Ocean, are combined with model temperatures from the European Centre for Medium-Range Weather Forecasts (ECMWF) and analyzed. Our main focus is to derive the distribution and temporal evolution of humidity with respect to ice within cirrus clouds and in their adjacent cloud-free air. We find that 34.1 % of in-cloud data points are supersaturated with respect to ice. Supersaturation is also detected in 6.8 % of the cloud-free data points. When the probability density of the relative humidity over ice (RHi) is calculated with respect to temperature for the in-cloud data points from the ML-CIRRUS dataset, there are two peaks: one around 225 K and close to saturation, RHi = 100 %, and a second one at colder temperatures around 218 K in subsaturation, RHi = 79 %. These two regions seem to represent two cirrus cloud categories: in situ formed and liquid origin. Regarding their vertical structure, most clouds have higher supersaturations close to the cloud top and become subsaturated near the cloud bottom. Finally, we find that the vertical structure of RHi within the clouds is also indicative of their life stage. RHi skewness tends to go from positive to negative values as the cloud ages. RHi modes are close to saturation in young clouds, supersaturated in mature clouds and subsaturated in dissipating clouds.

Transverse-pumping approach for a powerful single-mode Ti:sapphire laser for near infrared lidar applications.

We present a design of a transversely pumped and powerful Ti:sapphire laser, suitable for laser remote sensing in the near infrared (NIR) spectrum. Examining multiple pump configurations revealed that transversal pumping of Ti:sapphire crystals with a Nd:YAG laser (532 nm) from one or two sides yields maximum output power. Together with an optimized bow-tie resonator setup, we were able to extract up to 3 W (30 mJ, 100 Hz) of narrowband NIR laser emission from a cuboid 50 mm long Ti:sapphire crystal. Beam quality and divergence were determined as Mx2=1.1, My2=1.7, φx=0.5mrad, and φy=0.8mrad. The spectral purity (seeded, Pseed≥170µW) was better than 99.8%. Additionally, we show a two-wavelength setup applicable to atmospheric water vapor lidar. The principle performance of this theory of operation suggests a good chance of scalability towards significantly enhanced power of future Ti:sapphire lasers.

An OSSE Study of the Impact of Micropulse Differential Absorption Lidar (MPD) Water Vapor Profiles on Convective Weather Forecasting

Abstract The National Center for Atmospheric Research (NCAR) and Montana State University jointly developed water vapor micropulse differential absorption lidars (MPDs) that are a significant advance in eye-safe, unattended, lidar-based water vapor remote sensing. MPD is designed to provide continuous vertical water vapor profiles with high vertical (150 m) and temporal resolution (5 min) in the lower troposphere. This study aims to investigate MPD observation impacts and the scientific significance of MPDs for convective weather analyses and predictions using observation system simulation experiments (OSSEs). In this study, the Data Assimilation Research Testbed (DART) and the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model are used to conduct OSSEs for a case study of a mesoscale convective system (MCS) observed during the Plains Elevated Convection At Night (PECAN) experiment. A poor-performing control simulation that was drawn from a 40-member ensemble at 3-km resolution is markedly improved by assimilation of simulated observations drawn from a more skillful simulation that served as the nature run at 1-km resolution. In particular, assimilating surface observations corrected surface warm front structure errors, while MPD observations remedied errors in low- to midlevel moisture ahead of the MCS. Collectively, these analyses changes led to markedly improved short-term predictions of convection initiation, evolution, and precipitation of the MCS in the simulations on 15 July 2015. For this case study, the OSSE results indicate that a more dense MPD network results in better prediction performance for convective precipitation while degrading light precipitation prediction performance due to an imbalance of the analysis at large scales.

Characterizing warm atmospheric boundary layer over land by combining Raman and Doppler lidar measurements.

PBL plays a critical role in the atmosphere by transferring heat, moisture, and momentum. The warm PBL has a distinct diurnal cycle including daytime convective mixing layer (ML) and nighttime residual layer developments. Thus, for PBL characterization and process study, simultaneous determinations of PBL height (PBLH) and ML height (MLH) are necessary. Here, new approaches are developed to provide reliable PBLH and MLH to characterize warm PBL evolution. The approaches use Raman lidar (RL) water vapor mixing ratio (WVMR) and Doppler lidar (DL) vertical velocity measurements at the Southern Great Plains (SGP) atmospheric observatory, which was established by the Atmospheric Radiation Measurement (ARM) user facility. Compared with widely used lidar aerosol measurements for PBLH, WVMR is a better trace for PBL vertical mixing. For PBLH, the approach classifies PBL water vapor structures into a few general patterns, then uses a slope method and dynamic threshold method to determine PBLH. For MLH, wavelet analysis is used to re-construct 2-D variance from DL vertical wind velocity measurements according to the turbulence eddy size to minimize the impacts of gravity wave and eddy size on variance calculations; then, a dynamic threshold method is used to determine MLH. Remotely-sensed PBLHs and MLHs are compared with radiosonde measurements based on the Richardson number method. Good agreements between them confirm that the proposed new algorithms are reliable for PBLH and MLH characterization. The algorithms are applied to warm seasons' RL and ML measurements at the SGP site for five years to study warm season PBL structure and processes. The weekly composited diurnal evolutions of PBLHs and MLHs in warm climate were provided to illustrate diurnal and seasonal PBL evolutions. This reliable PBLH and MLH dataset will be valuable for PBL process study, model evolution, and PBL parameterization improvement.

Properties of Saharan Dust Versus Local Urban Dust—A Case Study

AbstractOn June 29–30, 2019, the Barcelona Dust Forecast Center with Non‐hydrostatic Multiscale Model (NMMB/BSC‐Dust) and the Navy Aerosol Analysis and Prediction System forecasted huge amounts of mineral dust over Poland. The Hybrid Single Particle Lagrangian Integrated Trajectory model confirmed uniquely fast (120 hr) long‐range air‐mass transport form North Africa to Poland. This remarkable dust event was observed using lidar at the Aerosol, Clouds and Trace Gases Research InfraStructure site in Warsaw, Central Poland; the only site equipped with Raman‐Mie polarization water vapor lidar in East‐Central Europe. The excellent capabilities of PollyXT lidar allowed to obtain an impressive number of 31 full sets of aerosol optical properties profiles, which enabled study of dust properties evolution on a rare hourly scale. The analyses were completed with the separation of fine and coarse mode dust particles form non‐dust particles using the POlarization‐LIdar PHOtometer Networking algorithm. Huge amount of an exceptionally pure mineral dust from Sahara measured in the free troposphere was characterized by a gradually decreasing coarse dust fraction (76%–21%) with a peak of fine dust fraction (67%) and particle linear depolarization ratio (26%) in the middle of the event. Within the boundary layer, a local urban dust mixed with pollution was observed with fine mode dust particles dominating (44%) and lower particle linear depolarization ratio (7.4%). The influx of pure mineral dust has been unique to this geographical region and will therefore be a reference point for future research and comparative studies.

Impact of assimilating lidar water vapour and temperature profiles with a hybrid ensemble transform Kalman filter: Three‐dimensional variational analysis on the convection‐permitting scale

AbstractWe discuss the analysis impact of the ensemble‐based assimilation of differential absorption lidar observed water vapour and Raman lidar observed temperature profiles into the Weather Research and Forecasting model at convection‐permitting scale. The impact of flow‐dependent background error covariance in the data assimilation (DA) system that uses the hybrid three‐dimensional variational (3DVAR) ensemble transform Kalman filter (ETKF) was compared to 3DVAR DA. The 3DVAR‐ETKF experiment resulted in a 50% lower temperature and water vapour RMSE than the 3DVAR experiment when taking the assimilated lidar data as reference and 26% (38%) lower water vapour (temperature) RMSE when comparing against independent radiosonde observations collocated with the lidar site. The planetary boundary‐layer height of the analyses compared to independent ceilometer data provided additional evidence of improvement. The 3DVAR analysis RMSE showed 140 m, whereas 3DVAR‐ETKF showed 60 m. Although limited to a single case study, we attribute these improvements to the flow‐dependent background error covariance matrix in the 3DVAR‐ETKF approach. The vertical profile measured from a single stationary lidar system established a spatial impact with a 100 km radius. This seems to indicate future assimilation of water vapour and temperature data from an operational lidar network. The assimilation impact persisted 7 hr into the forecast time compared with the ceilometer data and 4 hr with GPS observations.

A network of water vapor Raman lidars for improving heavy precipitation forecasting in southern France: introducing the WaLiNeAs initiative

Extreme heavy precipitation events (HPEs) pose a threat to human life but remain difficult to predict because of the lack of adequate high frequency and high-resolution water vapor (WV) observations in the low troposphere (below 3 km). To fill this observational gap, we aim at implementing an integrated prediction tool, coupling network measurements of WV profiles, and a numerical weather prediction model to precisely estimate the amount, timing, and location of rainfall associated with HPEs in southern France (struck by ~ 7 HPEs per year on average during the fall). The Water vapor Lidar Network Assimilation (WaLiNeAs) project will deploy a network of 6 autonomous Raman WV lidars around the Western Mediterranean to provide measurements with high vertical resolution and accuracy to be assimilated in the French Application of Research to Operations at Mesoscale (AROME-France) model, using a four-dimensional ensemble-variational approach with 15-min updates. This integrated prediction tool is expected to enhance the model capability for kilometer-scale prediction of HPEs over southern France up to 48 h in advance. The field campaign is scheduled to start early September 2022, to cover the period most propitious to heavy precipitation events in southern France. The Raman WV lidar network will be operated by a consortium of French, German, Italian, and Spanish research groups. This project will lead to recommendations on the lidar data processing for future operational exploitation in numerical weather prediction (NWP) systems.

Lagrangian matches between observations from aircraft, lidar and radar in a warm conveyor belt crossing orography

Abstract. Warm conveyor belts (WCBs) are important airstreams in extratropical cyclones, often leading to the formation of intense precipitation and the amplification of upper-level ridges. This study presents a case study that involves aircraft, lidar and radar observations in a WCB ascending from western Europe towards the Baltic Sea during the Hydrological Cycle in the Mediterranean Experiment (HyMeX) and T-NAWDEX-Falcon in October 2012, a preparatory campaign for the THORPEX North Atlantic Waveguide and Downstream Impact Experiment (T-NAWDEX). Trajectories were used to link different observations along the WCB, that is, to establish so-called Lagrangian matches between observations. To this aim, an ensemble of wind fields from the global analyses produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble of Data Assimilations (EDA) system were used, which allowed for a probabilistic quantification of the WCB occurrence and the Lagrangian matches. Despite severe air traffic limitations for performing research flights over Europe, the German Aerospace Center (DLR) Falcon successfully sampled WCB air masses during different phases of the WCB ascent. The WCB trajectories revealed measurements in two distinct WCB branches: one branch ascended from the eastern North Atlantic over southwestern France, while the other had its inflow in the western Mediterranean. Both branches passed across the Alps, and for both branches Lagrangian matches coincidentally occurred between lidar water vapour measurements in the inflow of the WCB south of the Alps, radar measurements during the ascent at the Alps and in situ aircraft measurements by Falcon in the WCB outflow north of the Alps. An airborne release experiment with an inert tracer could confirm the long pathway of the WCB from the inflow in the Mediterranean boundary layer to the outflow in the upper troposphere near the Baltic Sea several hours later. The comparison of observations and ensemble analyses reveals a moist bias in the analyses in parts of the WCB inflow but a good agreement of cloud water species in the WCB during ascent. In between these two observations, a precipitation radar measured strongly precipitating WCB air located directly above the melting layer while ascending at the southern slopes of the Alps. The trajectories illustrate the complexity of a continental and orographically influenced WCB, which leads to (i) WCB moisture sources from both the Atlantic and Mediterranean, (ii) different pathways of WCB ascent affected by orography, and (iii) locally steep WCB ascent with high radar reflectivity values that might result in enhanced precipitation where the WCB flows over the Alps. The linkage of observational data by ensemble-based WCB trajectory calculations, the confirmation of the WCB transport by an inert tracer and the model evaluation using the multi-platform observations are the central elements of this study and reveal important aspects of orographically modified WCBs.

Evaluation of Arctic Water Vapor Profile Observations from a Differential Absorption Lidar

The continuous measuring of the vertical profile of water vapor in the boundary layer using a commercially available differential absorption lidar (DIAL) has only recently been made possible. Since September 2018, a new pre-production version of the Vaisala DIAL system has operated at the Iqaluit supersite (63.74°N, 68.51°W), commissioned by Environment and Climate Change Canada (ECCC) as part of the Canadian Arctic Weather Science project. This study presents its evaluation during the extremely dry conditions experienced in the Arctic by comparing it with coincident radiosonde and Raman lidar observations. Comparisons over a one year period were strongly correlated (r > 0.8 at almost all heights) and exhibited an average bias of +0.13 ± 0.01 g/kg (DIAL-sonde) and +0.18 ± 0.02 g/kg (DIAL-Raman). Larger differences exhibiting distinct artifacts were found between 250 and 400 m above ground level (AGL). The DIAL’s observations were also used to conduct a verification case study of operational numerical weather prediction (NWP) models during the World Meteorological Organization’s Year of Polar Prediction. Comparisons to ECCC’s global environmental multiscale model (GEM-2.5 km and GEM-10 km) indicate good agreement with an average bias < 0.16 g/kg for the higher-resolution (GEM-2.5 km) models. All models performed significantly better during the winter than the summer, likely due to the winter’s lower water vapor concentrations and decreased variability. This study provides evidence in favor of using high temporal resolution lidar water vapor profile measurements to complement radiosonde observations and for NWP model verification and process studies.

Lidar Observations of a Mesoscale Moisture Transport Event Impacting Convection and Comparison to Rapid Refresh Model Analysis

AbstractThe 2015 Plains Elevated Convection at Night (PECAN) field campaign provided a wealth of intensive observations for improving understanding of interplay between the Great Plains low-level jet (LLJ), mesoscale convective systems (MCSs), and other phenomena in the nocturnal boundary layer. This case study utilizes PECAN ground-based Doppler and water vapor lidar and airborne water vapor lidar observations for a detailed examination of water vapor transport in the Great Plains. The chosen case, 11 July 2015, featured a strong LLJ that helped sustain an MCS overnight. The lidars resolved boundary layer moisture being transported northward, leading to a large increase in water vapor in the lowest several hundred meters above the surface in northern Kansas. A branch of nocturnal convection initiated coincident with the observed maximum water vapor flux. Radiosondes confirmed an increase in convective potential within the LLJ layer. Moist static energy (MSE) growth was generated by increasing moisture in spite of a temperature decrease in the LLJ layer. This unique dataset is also used to evaluate the Rapid Refresh (RAP) analysis model performance, comparing model output against the continuous lidar profiles of water vapor and wind. While the RAP analysis captured the large-scale trends, errors in water vapor mixing ratio were found ranging from 0 to 2 g kg−1 at the ground-based lidar sites. Comparison with the airborne lidar throughout the PECAN domain yielded an RMSE of 1.14 g kg−1 in the planetary boundary layer. These errors mostly manifested as contiguous dry or wet regions spanning spatial scales on the order of ten to hundreds of kilometers.

Effects of the prewhitening method, the time granularity, and the time segmentation on the Mann–Kendall trend detection and the associated Sen's slope

Abstract. The Mann–Kendall test associated with the Sen's slope is a very widely used non-parametric method for trend analysis. It requires serially uncorrelated time series, yet most of the atmospheric processes exhibit positive autocorrelation. Several prewhitening methods have therefore been designed to overcome the presence of lag-1 autocorrelation. These include a prewhitening, a detrending and/or a correction of the detrended slope and the original variance of the time series. The choice of which prewhitening method and temporal segmentation to apply has consequences for the statistical significance, the value of the slope and of the confidence limits. Here, the effects of various prewhitening methods are analyzed for seven time series comprising in situ aerosol measurements (scattering coefficient, absorption coefficient, number concentration and aerosol optical depth), Raman lidar water vapor mixing ratio, as well as tropopause and zero-degree temperature levels measured by radio-sounding. These time series are characterized by a broad variety of distributions, ranges and lag-1 autocorrelation values and vary in length between 10 and 60 years. A common way to work around the autocorrelation problem is to decrease it by averaging the data over longer time intervals than in the original time series. Thus, the second focus of this study evaluates the effect of time granularity on long-term trend analysis. Finally, a new algorithm involving three prewhitening methods is proposed in order to maximize the power of the test, to minimize the number of erroneous detected trends in the absence of a real trend and to ensure the best slope estimate for the considered length of the time series.

Spatio-temporal discrimination of molecular, aerosol and cloud scattering and polarization using a combination of a Raman lidar, Doppler cloud radar and microwave radiometer.

The combined data from the ESA Mobile Raman Polarization and Water Vapor Lidar (EMORAL), the LATMOS Bistatic Doppler Cloud Radar System for Atmospheric Studies (BASTA), and the INOE Microwave Radiometer (HATPRO-G2) have been used to explore the synergy for the spatio-temporal discrimination of polarization and molecular, aerosol and cloud scattering. The threshold-based methodology is proposed to perform an aerosol-cloud typing using the three instruments. It is demonstrated for 24 hours of observations on 10 June 2019 in Rzecin, Poland. A new scheme for target classification, developed collaboratively by the FUW and the OUC, can help determine molecules, aerosol (spherical, non-spherical, fine, coarse), cloud phase (liquid, ice, supercooled droplets) and precipitation (drizzle, rain). For molecular, aerosol, and cloud discrimination, the thresholds are set on the backward scattering ratio, the linear particle depolarization ratio and the backscatter colour ratio, all calculated from lidar signals. For the cloud phase and precipitation categorization, the thresholds are set on the reflectivity and the Doppler velocity derived from cloud radar signals. For boundary layer particles, precipitation, and supercooled droplets separation, the thresholds are set on the profiles of temperature and relative humidity obtained by the microwave radiometer. The algorithm is able to perform separation even under complicated meteorological situation, as in the presented case study.

The Canadian Arctic Weather Science Project: Introduction to the Iqaluit Site

AbstractThe goal of the Canadian Arctic Weather Science (CAWS) project is to conduct research into the future operational monitoring and forecasting programs of Environment and Climate Change Canada in the Arctic where increased economic and recreational activities are expected with enhanced transportation and search and rescue requirements. Due to cost, remoteness and vast geographical coverage, the future monitoring concept includes a combination of space-based observations, sparse in situ surface measurements, and advanced reference sites. A prototype reference site has been established at Iqaluit, Nunavut (63°45'N, 68°33'W), that includes a Ka-band radar, water vapor lidars (both in-house and commercial versions), multiple Doppler lidars, ceilometers, radiation flux, and precipitation sensors. The scope of the project includes understanding of the polar processes, evaluating new technologies, validation of satellite products, validation of numerical weather prediction systems, development of warning products, and communication of their risk to a variety of users. This contribution will provide an overview of the CAWS project to show some preliminary results and to encourage collaborations.

Observations of subtropical weather by a prototype water vapour LiDAR at Hong Kong Observatory

Observations of subtropical weather by a prototype water vapour LiDAR at Hong Kong Observatory