Water Vapor Mixing Ratio Profiles Research Articles

Scalable multiple changepoint detection for functional data sequences

AbstractWe propose the multiple changepoint isolation (MCI) method for detecting multiple changes in the mean and covariance of a functional process. We first introduce a pair of projections to represent the variability “between” and “within” the functional observations. We then present an augmented fused lasso procedure to split the projections into multiple regions robustly. These regions act to isolate each changepoint away from the others so that the powerful univariate CUSUM statistic can be applied region‐wise to identify the changepoints. Simulations show that our method accurately detects the number and locations of changepoints under many different scenarios. These include light and heavy tailed data, data with symmetric and skewed distributions, sparsely and densely sampled changepoints, and mean and covariance changes. We show that our method outperforms a recent multiple functional changepoint detector and several univariate changepoint detectors applied to our proposed projections. We also show that MCI is more robust than existing approaches and scales linearly with sample size. Finally, we demonstrate our method on a large time series of water vapor mixing ratio profiles from atmospheric emitted radiance interferometer measurements.

Distinct impacts of humidity profiles on physical properties and secondary formation of aerosols in Shanghai

As a key component in the atmosphere, the increase in water vapor content can aggravate air pollution by promoting hygroscopic growth and secondary formation of aerosols. In this study, water vapor mixing ratio (WVMR) profiles retrieved from Raman lidar at night in Shanghai were used to evaluate the accuracy of a maintained microwave radiometer (MWR) and reanalysis datasets (ERA5 from ECMWF and MERRA-2 from NASA). The relative humidity (RH) profiles from radiosonde and reanalysis datasets were used to explore the influence of humidity vertical distributions on aerosol physical properties and secondary formation. There was a large WVMR difference between MWR and Raman lidar, with a mean bias (MB) and a root mean square error (RMSE) being 2.47 g/kg and 3.29 g/kg, respectively. However, the WVMR from ERA5 and MERRA-2 showed excellent agreement (correlation coefficients were both 0.94, N = 11288 and 3619, respectively) with that from Raman lidar and high accuracy especially MERRA-2, which are more suitable for meteorological and climatic applications than MWR. It was found that RH had a significant effect on the vertical distribution of aerosol extinction coefficient (AEC). Aerosol hygroscopic growth caused by high RH resulted in an obvious enhancement of AEC at 355 nm below 610 m in summer. As a good indicator for determining contribution of aerosol secondary formation, large ratios of PM2.5 to CO (PM2.5/CO > 0.08) corresponded to high nitrogen oxidation ratios (NOR), sulfur oxidation ratios (SOR), and also high RH below 800 m during nighttime in winter, suggesting that RH and its vertical distribution played an important role in the aerosol secondary formation. The RH was high in the vertical direction when nitrate and sulfate mainly come from the secondary formation (NOR >0.1 or SOR >0.1), and in contrast to nitrate, the secondary formation for sulfates tended to depend on higher RH (>70%), which indicated that more stringent measures for nitrogen oxides emission reduction should be implemented in Shanghai to reduce the pollution of secondary aerosols in the future.

Measurements of atmospheric aerosol hygroscopic growth based on multi-channel Raman-Mie lidar

In this study, an effective method with a multi-channel Raman-Mie lidar has been proposed to detect the hygroscopic growth of atmospheric aerosol in the boundary layer, and the effect of aerosol hygroscopic growth on the optical and microphysical properties of aerosol particles is studied. The vertical relative humidity profile is obtained by combining calibrated water vapor mixing ratio and temperature profiles from Raman-Mie lidar. Two cases were selected in Hefei to analyze the aerosol hygroscopic growth. Case studies show that the increase of the backscatter coefficient of aerosol is consistent with the increase of relative humidity, and the decrease of Ångstrom exponent indicates the increasing aerosol particle size due to the hygroscopic growth process. The Kasten model is used to fit the aerosol hygroscopic enhancement factor, and the b value from the Kasten parameterization for Case II is much larger than that for Case I, which shows the stronger hygroscopicity in Case II. The results indicate that the multi-channel Raman-Mie lidar has potential in the measurements of the hygroscopic growth of aerosols.

Improvement of precipitable water vapour and water vapour mixing ratio profile in atmospheric infrared sounder retrievals: differential linear adjustment model

ABSTRACT Knowledge of both the horizontal and vertical distributions of atmospheric water vapour is pivotal to understand the Earth’s radiative balance. The Atmospheric Infrared Sounder (AIRS) provides an opportunity to monitor both precipitable water vapour (PWV) measurements and water vapour mixing ratio (MR) profiles. In this study, we incorporate a differential linear adjust model (DLAM) by using the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim) to adjust the AIRS water vapour retrievals. The performance of the differential linear adjustment model (DLAM) is assessed by comparing AIRS water vapour retrievals with spatio-temporally synchronized radiosonde (RS) observations. Taking RS data as the reference, RMS of the DLAM-adjusted PWV is reduced by about 16%, and that of water vapour MR is decreased by 17% to 8% from 1000 to 500 hPa. We also find that the DLAM appears to be affected by the generally larger uncertainties of the a priori water vapour MR at upper tropospheric levels, and the DLAM-derived water vapour MR improvement is generally greater from 1000 to 850 hPa than 700 to 500 hPa. Moreover, the potential reason for the effectiveness of the DLAM on AIRS water vapour retrievals adjustment may be the deviation of the differential water vapour information derived by the differential process is significantly reduced in the incorporated model.

Compact fiber-optic spectroscopic design and its validation in atmospheric water vapor Raman lidar

Spectroscopic systems with compact structure, high reliability, and high stability will push forward the application of lidar in the meteorological and environmental fields as well as space-based and space-borne lidar. Recently, we have successfully designed and verified a new robust fiber-optic spectroscopic Raman lidar for atmospheric water vapor measurements. The compact fiber-optic Raman spectroscopic design was proposed with a cascaded fiber Fabry–Perot filter and a fiber broadband filter, which features an all-fiber-connection structure and a high-spectral-resolution spectrum. These fiber-optic filters have been successfully fabricated in the visible region and matched each other in individual all-fiber Raman channels. The performance of the all-fiber spectroscopic system is measured experimentally, and it is demonstrated that the spectral output from the individual fiber Raman channel provides a bandwidth of 3–4 nm, a central wavelength of 660 nm or 606.7 nm, and an efficient rejection rate of more than > --> 10 7 , and it can thus achieve the extraction of vibrational Raman scattering signals from water vapor and from nitrogen molecules in lidar returns and simultaneously reject elastic scattering signals. Furthermore, the fiber-optic spectroscopic Raman lidar was developed at Xi’an University of Technology in Xi’an, China (34.233°N, 108.911°E), and preliminary exploration was carried out for water vapor measurements. Two measurement examples are analyzed to validate the lidar performance and the retrieved water vapor mixing ratio profiles, and its comparisons with Weather Research and Forecasting (WRF) model data also verified the correctness of the lidar data. Despite the limitation of low coupling efficiency by a single-mode optical fiber, atmospheric water vapor measurements up to 700 m were achieved during nighttime under conditions of a 150 mJ laser pulse energy, a 600 mm telescope, and a 5 min integration time. The results validated the concept of all-fiber spectroscopic design and its feasibility for atmospheric water vapor measurements by Raman lidar. The compact fiber-structure characteristics and excellent optical properties provide a new solution to the new spectroscopic technique for operational applications of lidar and space-borne lidar.

Temperature and water vapour measurements in the framework of the Network for the Detection of Atmospheric Composition Change (NDACC)

Abstract. The BASIL Raman lidar system entered the International Network for the Detection of Atmospheric Composition Change (NDACC) in 2012. Since then, measurements have been carried out routinely on a weekly basis. This paper reports specific measurement results from this effort, with a dedicated focus on temperature and water vapour profile measurements. The main objective of this research effort is to provide a characterisation of the system performance. The results illustrated in this publication demonstrate the ability of BASIL to perform measurements of the temperature profile up to 50 km and of the water vapour mixing ratio profile up to 15 km, when considering an integration time of 2 h and a vertical resolution of 150–600 m; the mean measurement accuracy, determined based on comparisons with simultaneous and co-located radiosondes, is 0.1 K (for the temperature profile) and 0.1 g kg−1 (for the water vapour mixing ratio profile) up to the upper troposphere. The relative humidity profiling capability up to the tropopause is also demonstrated by combining simultaneous temperature and water vapour profile measurements. Raman lidar measurements are compared with measurements from additional instruments, such as radiosondes and satellite sensors (IASI and AIRS), as well as with model reanalyses data (ECMWF and ECMWF-ERA). We focused our attention on six case studies collected during the first 2 years of system operation (November 2013–October 2015). Comparisons between BASIL and the different sensor/model data in terms of the water vapour mixing ratio indicate biases in the altitudinal interval between 2 and 15 km that are always within ±1 g kg−1 (or ±50 %), with minimum values being observed in the comparison between BASIL and radiosonde measurements (±20 % up to 15 km). Results also indicate a vertically averaged mean mutual bias of −0.026 g kg−1 (or −3.8 %), 0.263 g kg−1 (or 30.0 %), 0.361 g kg−1 (or 23.5 %), −0.297 g kg−1 (or −25 %) and −0.296 g kg−1 (or −29.6 %) when comparing BASIL with radiosondes, IASI, AIRS, ECMWF and ECMWF-ERA respectively. The vertically averaged mean absolute mutual biases are somewhat higher, i.e. 0.05 g kg−1(or 16.7 %), 0.39 g kg−1 (or 23.0 %), 0.57 g kg−1 (or 23.5 %), 0.32 g kg−1 (or 29.6 %) and 0.52 g kg−1 (or 53.3 %), when comparing BASIL with radiosondes, IASI, AIRS, ECMWF and ECMWF-ERA respectively. The comparisons in terms of temperature measurements indicate mutual biases in the altitudinal interval between 3 and 30 km that are always within ±3 K, with minimum values being observed in the comparison between BASIL and radiosonde measurements (±2 K within this same altitudinal interval). Results also reveal mutual biases within ±3 K up to 50 km for most sensor/model pairs. Furthermore, a vertically averaged mean mutual bias of −0.03, 0.21, 1.95, 0.14 and 0.43 K is found between BASIL and the radiosondes, IASI, AIRS, ECMWF and ECMWF-ERA respectively. The vertically averaged absolute mean mutual biases between BASIL and the radiosondes, IASI, AIRS, ECMWF and ECMWF-ERA are 1.28, 1.30, 3.50, 1.76 and 1.63 K respectively. Based on the available dataset and benefiting from the fact that the BASIL Raman lidar could be compared with all other sensor/model data, it was possible to estimate the overall bias of all sensors/datasets: −0.04 g kg−1 ∕ 0.19 K, 0.20 g kg−1 ∕ 0.22 K, −0.31 g kg−1 ∕ −0.02 K, −0.40 g kg−1 ∕ −1.76 K, 0.25 g kg−1 ∕ 0.04 K and 0.25 g kg−1 ∕ −0.24 K for the water vapour mixing ratio/temperature profile measurements carried out by BASIL, the radiosondes, IASI, AIRS, ECMWF and ECMWF-ERA respectively.

Water Vapour and Temperature Measurements by Raman Lidar in the Frame of the NDACC

In November 2012, the University of BASILicata Raman Lidar system (BASIL) was approved to enter the International Network for the Detection of Atmospheric Composition Change (NDACC). Since then measurements were routinely carried out on a once per week basis. This paper illustrates specific measurement examples from this effort, with a dedicated focus on temperature and water vapour measurements, with the ultimate goal to provide a characterization of the system performance. Case studies illustrated in this paper demonstrate the ability of BASIL to perform measurements of the temperature profile up to 50 km and of the water vapour mixing ratio profile up to 15 km, based on an integration time of 2 hours and a vertical resolution of 150 m, with measurement bias not exceeding 0.1 K and 0.1 g kg−1, respectively. Raman lidar measurements are compared with measurements from additional instruments, such as radiosondings and satellite sensors (IASI and AIRS), and with model re-analyses data (ECMWF and ECMWF-ERA). Comparisons in this paper cover the altitude interval up to 15 km for water vapour mixing ratio and up to 50 km for the temperature. Comparisons between BASIL and the different sensor/model data in terms of water vapour mixing ratio indicate a mean absolute/relative bias of -0.024 g kg−1(or -3.9 %), 0.342 g kg−1(or 36.8 %), 0.346 g kg−1 (or 37.5 %), -0.297 g kg−1 (or -25 %), -0.381 g kg−1 (or -31 %), when compared with radisondings, AIRS, IASI, ECMWF, ECMWF-ERA, respectively. For what concerns the comparisons in terms of temperature measurements, these indicate a mean absolute bias between BASIL and the radisondings, AIRS, IASI, ECMWF, ECMWF-ERA of -0.04, 1.99, 0.48, 0.14, 0.62 K, respectively. Based on the available dataset and benefiting from the circumstance that the Raman lidar BASIL could be compared with all other sensor/model data, it has been possible to estimate the absolute bias of all sensors/datasets, this being 0.004 g kg−1/0.30 K, 0.021 g kg−1/-0.34 K, -0.35 g kg−1/0.18 K, -0.346 g kg−1/-1.63 K, 0.293 g kg−1/-0.16 K and 0.377 g kg−1/0.32 K in terms of water vapour mixing ratio/temperature for BASIL, the radisondings, IASI, AIRS, ECMWF, ECMWF-ERA, respectively.

CO2 Profiling by Space-Borne Raman Lidar

As clearly reported in the IPCC fifth Assessment Report, CO2 emissions are already producing destructive effects to the plant ecosystem through the alteration of soil-atmosphere interaction mechanisms. Although the space and ground network for CO2 monitoring has regularly expanded over the past 50 years, it does not guarantee the necessary spatial and temporal resolution needed for a quantitative analysis of sources and sinks. For the purpose of estimating forests’ carbon capturing capabilities, accurate measurements of CO2 gradients between the forest floor and the top of the canopy, which ultimately translates into the capability to measure CO2 concentration profiles. Space sensors provide CO2 measurements above forest canopies, which do not allow to properly estimate Gross Primary Production (GPP). These observational gaps could be addressed with an active remote sensing system in space based on the vibrational Raman lidar technique. CO2 profile measurements are possible, together with simultaneous measurements of the temperature and water vapour mixing ratio profile and a variety of additional variables (aerosol backscatter profile, aerosol extinction profile, PBL depth, cloud top and base heights, cloud optical depth). An assessment of the expected performance of the system has been performed based on the application of an analytical simulation model developed at University of Basilicata.

Assimilation of Lidar Water Vapour Mixing Ratio and Temperature Profiles into a Convection-Permitting Model

Assimilation of Lidar Water Vapour Mixing Ratio and Temperature Profiles into a Convection-Permitting Model

Water Vapor Mixing Ratio Distribution Inversion by Raman Lidar in Beijing

Water Aerosol Raman Lidar-II is an active detection instrument with high temporal and spatial resolution at Nanjiao observation station, and that could continuous water vapor mixing ratio (WVMR) measurements. WVMR profiles inversion from lidar data and water ratio retrieved from radiosonde data are in good agreement. The statistical results of the vertical distribution of WVMR indicate that WVMR seasonal mean distribution is consistent with precipitation. In addition, WVMR in Nanjiao station is related to total cloud cover.

A practical information-centered technique to remove a priori information from lidar optimal-estimation-method retrievals

Abstract. Lidar retrievals of atmospheric temperature and water vapor mixing ratio profiles using the optimal estimation method (OEM) typically use a retrieval grid with a number of points larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from their respective a priori values or profiles, which can affect the results in the higher altitudes of the temperature and water vapor profiles due to decreasing signal-to-noise ratios. The extent of this influence can be estimated using the retrieval's averaging kernels. The removal of formal a priori information from the retrieved profiles in the regions of prevailing a priori effects is desirable, particularly when these greatest heights are of interest for scientific studies. We demonstrate here that removal of a priori information from OEM retrievals is possible by repeating the retrieval on a coarser grid where the retrieval is stable even without the use of formal prior information. The averaging kernels of the fine-grid OEM retrieval are used to optimize the coarse retrieval grid. We demonstrate the adequacy of this method for the case of a large power-aperture Rayleigh scatter lidar nighttime temperature retrieval and for a Raman scatter lidar water vapor mixing ratio retrieval during both day and night.

Improvements to the AERIoe Thermodynamic Profile Retrieval Algorithm

Temperature and humidity profiles in the atmospheric boundary layer (i.e., from the surface to 3 km) can be retrieved from ground-based spectral infrared observations made by the atmospheric emitted radiance interferometer (AERI) at high temporal and moderate vertical resolution. However, the retrieval is an ill-posed problem, and thus there are multiple thermodynamic solutions that might satisfy the observed radiances. Previous work developed a physical-iterative method called AERIoe that retrieved temperature and water vapor mixing ratio profiles from these radiance observations in both clear and cloudy conditions. The AERIoe algorithm was modified to enforce two physical constraints, namely that the derived relative humidity must be less than 100% and that the potential temperature must be monotonically increasing with height above some thin potentially subadiabatic layer after each iteration. Furthermore, additional observations including in situ surface meteorology, numerical weather prediction model output, microwave brightness temperatures, and partial profiles of water vapor from a Raman lidar were incorporated into the observation vector of the retrieval along with the infrared radiance observations. The addition of these new observations markedly improved the accuracy of the temperature profiles, especially above 2 km, and the water vapor profiles relative to radiosondes. These improvements are seen using cases from the tropics, mid-latitudes, and Arctic.

Water Vapor Calibration: Using a Raman Lidar and Radiosoundings to Obtain Highly Resolved Water Vapor Profiles

We revised the calibration of a water vapor Raman lidar by co-located radiosoundings for a site in the high European Arctic. For this purpose, we defined robust criteria for a valid calibration. One of these criteria is the logarithm of the water vapor mixing ratio between the sonde and the lidar. With an error analysis, we showed that for our site correlations smaller than 0.95 could be explained neither by noise in the lidar nor by wrong assumptions concerning the aerosol or Rayleigh extinction. However, highly variable correlation coefficients between sonde and consecutive lidar profiles were found, suggesting that small scale variability of the humidity was our largest source of error. Therefore, not all co-located radiosoundings are useful for lidar calibration. As we assumed these changes to be non-systematic, averaging over several independent measurements increased the calibration’s quality. The calibration of the water vapor measurements from the lidar for individual profiles varied by less than ±5%. The seasonal median, used for calibration in this study, was stable and reliable (confidence ±1% for the season with most calibration profiles). Thus, the water vapor mixing ratio profiles from the Koldewey Aerosol Raman Lidar (KARL) are very accurate. They show high temporal variability up to 4 km altitude and, therefore, provide additional, independent information to the radiosonde.

Investigation and Analysis of All-Day Atmospheric Water Vapor Content over Xi’an Using Raman Lidar and Sunphotometer Measurements

All-day atmospheric water vapor content measurements determined by Raman lidar and a sunphotometer were combined to investigate the all-day variation characteristics in the water vapor distribution in Xi’an, China (34.233°N, 108.911°E). To enhance the daytime lidar performance, the wavelet threshold de-noising method is used to filter out the strong solar background light, and effective denoised results are demonstrated with the following optimization: wavelet sym6, the improved threshold function, and the improved threshold selection. The denoised system signal-to-noise ratio (SNR) for the water vapor daytime measurement is validated, with an enhancement of ~3.4 times up to a height of 3 km compared to that of the original signal. The time series of the atmospheric water vapor mixing ratio profiles and the obtained precipitable water vapor (PWV) measured by Raman lidar are used to reveal the temporal and spatial variations in water vapor, and the comparisons with the total column water vapor content (TCWV) measured by a sunphotometer validate the daytime variation trend of the water vapor. All-day continuous observations clearly present a consistent variation trend in the water vapor between the sunphotometer and Raman lidar measurements. The correlation analysis between TCWV and PWV at the layers below 850 hPa and below 700 hPa yields a good positive correlation coefficient (>0.75), indicating that PWV determination in the bottom layer by Raman lidar can directly reflect the variations in the total water vapor content. Moreover, different diurnal variation trends in water vapor are also observed, that is, a downward trend from the afternoon to the night, or a tendency of being high in the morning and afternoon and low at noon, demonstrating the high temporal-spatial variation characteristics of water vapor and close correlation with weather changes. The results reflected and validated that the diurnal variation in water vapor is complicated and can be an indicator of the weather to a certain extent.

Hygroscopic growth study in the framework of EARLINET during the SLOPE I campaign: synergy of remote sensing and in situ instrumentation

Abstract. This study focuses on the analysis of aerosol hygroscopic growth during the Sierra Nevada Lidar AerOsol Profiling Experiment (SLOPE I) campaign by using the synergy of active and passive remote sensors at the ACTRIS Granada station and in situ instrumentation at a mountain station (Sierra Nevada, SNS). To this end, a methodology based on simultaneous measurements of aerosol profiles from an EARLINET multi-wavelength Raman lidar (RL) and relative humidity (RH) profiles obtained from a multi-instrumental approach is used. This approach is based on the combination of calibrated water vapor mixing ratio (r) profiles from RL and continuous temperature profiles from a microwave radiometer (MWR) for obtaining RH profiles with a reasonable vertical and temporal resolution. This methodology is validated against the traditional one that uses RH from co-located radiosounding (RS) measurements, obtaining differences in the hygroscopic growth parameter (γ) lower than 5 % between the methodology based on RS and the one presented here. Additionally, during the SLOPE I campaign the remote sensing methodology used for aerosol hygroscopic growth studies has been checked against Mie calculations of aerosol hygroscopic growth using in situ measurements of particle number size distribution and submicron chemical composition measured at SNS. The hygroscopic case observed during SLOPE I showed an increase in the particle backscatter coefficient at 355 and 532 nm with relative humidity (RH ranged between 78 and 98 %), but also a decrease in the backscatter-related Ångström exponent (AE) and particle linear depolarization ratio (PLDR), indicating that the particles became larger and more spherical due to hygroscopic processes. Vertical and horizontal wind analysis is performed by means of a co-located Doppler lidar system, in order to evaluate the horizontal and vertical dynamics of the air masses. Finally, the Hänel parameterization is applied to experimental data for both stations, and we found good agreement on γ measured with remote sensing (γ532=0.48±0.01 and γ355=0.40±0.01) with respect to the values calculated using Mie theory (γ532=0.53±0.02 and γ355=0.45±0.02), with relative differences between measurements and simulations lower than 9 % at 532 nm and 11 % at 355 nm.

Calibration of Raman lidar water vapor profiles by means of AERONET photometer observations and GDAS meteorological data

Abstract. We present a practical method to continuously calibrate Raman lidar observations of water vapor mixing ratio profiles. The water vapor profile measured with the multiwavelength polarization Raman lidar PollyXT is calibrated by means of co-located AErosol RObotic NETwork (AERONET) sun photometer observations and Global Data Assimilation System (GDAS) temperature and pressure profiles. This method is applied to lidar observations conducted during the Cyprus Cloud Aerosol and Rain Experiment (CyCARE) in Limassol, Cyprus. We use the GDAS temperature and pressure profiles to retrieve the water vapor density. In the next step, the precipitable water vapor from the lidar observations is used for the calibration of the lidar measurements with the sun photometer measurements. The retrieved calibrated water vapor mixing ratio from the lidar measurements has a relative uncertainty of 11 % in which the error is mainly caused by the error of the sun photometer measurements. During CyCARE, nine measurement cases with cloud-free and stable meteorological conditions are selected to calculate the precipitable water vapor from the lidar and the sun photometer observations. The ratio of these two precipitable water vapor values yields the water vapor calibration constant. The calibration constant for the PollyXT Raman lidar is 6.56 g kg−1 ± 0.72 g kg−1 (with a statistical uncertainty of 0.08 g kg−1 and an instrumental uncertainty of 0.72 g kg−1). To check the quality of the water vapor calibration, the water vapor mixing ratio profiles from the simultaneous nighttime observations with Raman lidar and Vaisala radiosonde sounding are compared. The correlation of the water vapor mixing ratios from these two instruments is determined by using all of the 19 simultaneous nighttime measurements during CyCARE. Excellent agreement with the slope of 1.01 and the R2 of 0.99 is found. One example is presented to demonstrate the full potential of a well-calibrated Raman lidar. The relative humidity profiles from lidar, GDAS (simulation) and radiosonde are compared, too. It is found that the combination of water vapor mixing ratio and GDAS temperature profiles allow us to derive relative humidity profiles with the relative uncertainty of 10–20 %.

Study and mitigation of calibration factor instabilities in a water vapor Raman lidar

Abstract. We have investigated calibration variations in the Rameau water vapor Raman lidar. This lidar system was developed by the Institut National de l'Information Géographique et Forestière (IGN) together with the Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS). It aims at calibrating Global Navigation Satellite System (GNSS) measurements for tropospheric wet delays and sounding the water vapor variability in the lower troposphere. The Rameau system demonstrated good capacity in retrieving water vapor mixing ratio (WVMR) profiles accurately in several campaigns. However, systematic short-term and long-term variations in the lidar calibration factor pointed to persistent instabilities. A careful testing of each subsystem independently revealed that these instabilities are mainly induced by mode fluctuations in the optic fiber used to couple the telescope to the detection subsystem and by the spatial nonuniformity of the photomultiplier photocathodes. Laboratory tests that replicate and quantify these instability sources are presented. A redesign of the detection subsystem is presented, which, combined with careful alignment procedures, is shown to significantly reduce the instabilities. Outdoor measurements were performed over a period of 5 months to check the stability of the modified lidar system. The calibration changes in the detection subsystem were monitored with lidar profile measurements using a common nitrogen filter in both Raman channels. A short-term stability of 2–3 % and a long-term drift of 2–3 % per month are demonstrated. Compared to the earlier Development of Methodologies for Water Vapour Measurement (DEMEVAP) campaign, this is a 3-fold improvement in the long-term stability of the detection subsystem. The overall water vapor calibration factors were determined and monitored with capacitive humidity sensor measurements and with GPS zenith wet delay (ZWD) data. The changes in the water vapor calibration factors are shown to be fairly consistent with the changes in the nitrogen calibration factors. The nitrogen calibration results can be used to correct the overall calibration factors without the need for additional water vapor measurements to within 1 % per month.

Six-channel multi-wavelength polarization Raman lidar for aerosol and water vapor profiling.

Aerosols and water vapor are important atmospheric components, and have significant effects on both atmospheric energy conversion and climate formation. They play the important roles in balancing the radiation budget between the atmosphere and Earth, while water vapor also directly affects rainfall and other weather processes. To further research atmospheric aerosol optical properties and water vapor content, an all-time six-channel multi-wavelength polarization Raman lidar has been developed at Beifang University of Nationalities. In addition to 1064, 532, and 355nm Mie scattering channels, the lidar has a polarization channel for 532nm return signals, a 660nm water vapor channel, and a 607nm nitrogen detection channel. Experiments verified the lidar's feasibility and return signals from six channels were detected. Using inversion algorithms, extinction coefficient profiles at 1064, 532 and 355nm, Ångström exponent profiles, depolarization ratio profiles, and water vapor mixing ratio profiles were all obtained. The polarization characteristics and water vapor content of cirrus clouds, the polarization characteristics of dusty weather, and the water vapor profiles over different days were also analyzed. Results show that the lidar has the full-time detection capability for atmospheric aerosol optical properties and water vapor profiles, and real-time measurements of aerosols and water vapor over the Yinchuan area were realized, providing important information for studying the environmental quality and climate change in this area.

Nonlinear response of hail precipitation rate to environmental moisture content: A real case modeling study of an episodic midlatitude severe convective event

AbstractThe dependence of hail production on initial moisture content in a simulated midlatitude episodic convective event occurred in northeast China on 10–11 June 2005 was investigated using the Weather Research and Forecasting (WRF) model with a double‐moment microphysics scheme where both graupel and hail are considered. Three sensitivity experiments were performed by modifying the initial water vapor mixing ratio profile to 90% (“Q−10%”), 105% (“Q+5%”), and 110% (“Q+10%”) of the initial conditions used for the control simulation. It was found that increasing the initial water vapor content caused the hail and total precipitation rates to increase during the first 5 h. The precipitation response to increasing water vapor content was monotonic for this first episode; however, for the event's second episode, the hail precipitation rate responds to the initial water vapor profile nonlinearly, while the total precipitation rate responds mostly monotonically. In particular, simulation Q+5% achieves the largest hail production rate while simulation Q+10% has the largest total precipitation rate. In contrast, during the second episode simulation Q−10% has the strongest vertical motion, produces the most cloud ice and snow, but has the lowest hail production. Analysis shows that increasing the initial moisture content directly increases the precipitation during the first episode, which subsequently induces a stronger, longer‐lasting cold pool that limits the development of deep convection during the second episode.

Raman lidar water vapor profiling over Warsaw, Poland

Water vapor mixing ratio and relative humidity profiles were derived from the multi-wavelength Raman PollyXT lidar at the EARLINET site in Warsaw, using the Rayleigh molecular extinction calculation based on atmospheric temperature and pressure from three different sources: i) the standard atmosphere US 62, ii) the Global Data Assimilation System (GDAS) model output, and iii) the WMO 12374 radiosoundings launched at Legionowo. With each method, 136 midnight relative humidity profiles were obtained for lidar observations from July 2013 to August 2015. Comparisons of these profiles showed in favor of the latter method (iii), but it also indicated that the other two data sources could replace it, if necessary. Such use was demonstrated for an automated retrieval of water vapor mixing ratio from dusk until dawn on 19/20 March 2015; a case study related to an advection of biomass burning aerosol from forest fires over Ukraine. Additionally, an algorithm that applies thresholds to the radiosounding relative humidity profiles to estimate macro-physical cloud vertical structure was used for the first time on the Raman lidar relative humidity profiles. The results, based on a subset of 66 profiles, indicate that below 6km cloud bases/tops can be successfully obtained in 53% and 76% cases from lidar and radiosounding profiles, respectively. Finally, a contribution of the lidar derived mean relative humidity to cloudy conditions within the range of 0.8 to 6.2km, in comparison to clear-sky conditions, was estimated.