Estimation Of Liquid Water Content Research Articles

In situ estimation of effective liquid water content on a wind turbine using a thermal based sensor

To correctly assess the performance of wind turbine ice protection systems (IPS), a complete overview of the meteorological conditions is needed. The same would prove helpful in the usage of the alternative operational control strategies during icing events. A crucial factor to consider is the estimation of the liquid water content (LWC). This parameter has a significant impact on the thermal behavior of IPS and therefore their efficiency. Finding a reliable, robust and accessible method for estimating the LWC on wind farms is a challenge that has yet to be solved. Wind farm operators frequently have to rely on visual observations to guide their decisions. This paper proposes a method for estimating the effective LWC in wind farms using a thermal based ice sensor. This proposed method was calibrated using the equivalent effective LWC obtained from image analysis of ice accretion on a reference cylinder. Over the winter of 2018–2019, on a Canadian site, 8 icing events were retained to provide calibration data. An increase of more than 10 mm of ice frontal thickness was observed during the daylight for all the events. The thermal based ice sensor proved to be coherent with the camera observations while presenting a more robust method with better sensitivity and accuracy. Having a reliable LWC estimation method not only presents an advantage for wind turbine operation but could also potentially help the site assessment process where the numerical weather predictions are not able to capture intra-site variations of the icing severity.

The Vertical Structure of Liquid Water Content in Shallow Clouds as Retrieved From Dual‐Wavelength Radar Observations

AbstractObservations collected over 3 months by the beam‐matched second‐generation Ka/W band Scanning Cloud Radar located at the Atmospheric Radiation Measurement Program Eastern North Atlantic observatory are used to advance existing liquid water content (LWC) retrieval techniques, quantify retrieval uncertainty, and subsequently characterize the impact of cloud dynamics and rain rates on the vertical distribution of LWC in boundary layer clouds both precipitating and broken. A threefold technique is proposed that involves (1) temporally averaging measured radar reflectivities collected at two wavelengths to 30‐s resolution, (2) smoothing via fitting a second‐degree polynomial to their dual‐wavelength ratios within 187.5‐m vertical overlapping sliding windows, and (3) averaging the multiple LWC estimates produced at each height. It is estimated that this technique reduced LWC retrieval uncertainty to 0.10–0.65 g/m3, depending on cloud thickness. Although individual retrievals remained noisy, statistics on subgroups of height‐normalized LWC profiles show that, on average, the vertical distributions of LWC in most of the observed clouds followed a linear relationship with a degree of adiabaticity ranging from 0.6 to 0.2 for 200‐ to 600‐m thick clouds. However, nonlinear LWC profiles were present in subgroups of cloud segments presenting intense (0.1–0.5 mm/hr) drizzle rates where LWC was observed to pool near cloud base and in subgroups of cloud segments within strong (0.6 m/s) downdrafts near cloud top where LWC was coincidently reduced. This nonlinearity is inconsistent with the use of adiabatic cloud assumptions for process studies and supports further development of retrievals like the one proposed.

Assessing the mechanisms governing the daytime evolution of marine stratocumulus using large‐eddy simulation

Large‐eddy simulation (LES) output for a case of thin stratocumulus off the coast of California is examined in a mixed‐layer analysis framework to identify the specific mechanisms responsible for governing the evolution of the cloud system. An equation for cloud‐base height tendency isolates the individual cloud‐modulating mechanisms that control the evolution of boundary‐layer liquid‐water static energy (Sl) and total water mixing ratio (qT). With a suitable spin‐up procedure, the control simulation performs admirably compared with observed estimates of liquid water content, vertical velocity variance, and radiative fluxes sampled during an aircraft field campaign. Investigation of the cloud response to various environmental forcing scenarios was addressed through a suite of sensitivity simulations, including variations in subsidence velocity, surface fluxes, wind shear near the inversion, and radiative forcing. In the control simulation, rising cloud‐base tendencies are associated with entrainment warming/drying and short‐wave absorption, whereas lowering cloud‐base tendencies are driven by long‐wave cooling. Even in the presence of substantial afternoon solar heating, entrainment fluxes remained active. The thin cloud demonstrated unexpected resiliency, with mixed‐layer analysis indicating that, as the short‐wave flux decreases later in the afternoon, the relative contribution of long‐wave cooling often becomes large enough to offset entrainment warming/drying and result in a reversal of cloud‐base tendency. The evolution of cloud‐base tendency is found to be insensitive to the net radiative flux divergence for most of the simulations (liquid water path ranging from ~10–50 g/m2). Error analysis in comparison with LES Sl and qT budgets suggests that our method of entrainment flux calculation could be improved by a more complete understanding of entrainment‐layer physics.

Estimation of Liquid Water Content of Snow Surface by Spectral Reflectance

This study measures the spectral reflectance from snow with known liquid water content (LWC) in a climate chamber using two optical sensors, a near-infrared (NIR) spectrometer and a Road eye sensor ...

Proof of Concept: Development of Snow Liquid Water Content Profiler Using CS650 Reflectometers at Caribou, ME, USA.

The quantity of liquid water in the snowpack defines its wetness. The temporal evolution of snow wetness’s plays a significant role in wet-snow avalanche prediction, meltwater release, and water availability estimations and assessments within a river basin. However, it remains a difficult task and a demanding issue to measure the snowpack’s liquid water content (LWC) and its temporal evolution with conventional in situ techniques. We propose an approach based on the use of time-domain reflectometry (TDR) and CS650 soil water content reflectometers to measure the snowpack’s LWC and temperature profiles. For this purpose, we created an easily-applicable, low-cost, automated, and continuous LWC profiling instrument using reflectometers at the Cooperative Remote Sensing Science and Technology Center-Snow Analysis and Field Experiment (CREST-SAFE) in Caribou, ME, USA, and tested it during the snow melt period (February–April) immediately after installation in 2014. Snow Thermal Model (SNTHERM) LWC simulations forced with CREST-SAFE meteorological data were used to evaluate the accuracy of the instrument. Results showed overall good agreement, but clearly indicated inaccuracy under wet snow conditions. For this reason, we present two (for dry and wet snow) statistical relationships between snow LWC and dielectric permittivity similar to Topp’s equation for the LWC of mineral soils. These equations were validated using CREST-SAFE in situ data from winter 2015. Results displayed high agreement when compared to LWC estimates obtained using empirical formulas developed in previous studies, and minor improvement over wet snow LWC estimates. Additionally, the equations seemed to be able to capture the snowpack state (i.e., onset of melt, medium, and maximum saturation). Lastly, field test results show advantages, such as: automated, continuous measurements, the temperature profiling of the snowpack, and the possible categorization of its state. However, future work should focus on improving the instrument’s capability to measure the snowpack’s LWC profile by properly calibrating it with in situ LWC measurements. Acceptable validation agreement indicates that the developed snow LWC, temperature, and wetness profiler offers a promising new tool for snow hydrology research.

Hydrometeor Mixing Ratio Retrievals for Storm-Scale Radar Data Assimilation: Utility of Current Relations and Potential Benefits of Polarimetry

Abstract The assimilation of radar data into storm-scale numerical weather prediction models has been shown to be beneficial for successfully modeling convective storms. Because of the difficulty of directly assimilating reflectivity (Z), hydrometeor mixing ratios, and sometimes rainfall rate, are often retrieved from Z observations using retrieval relations, and are assimilated as state variables. The most limiting (although widely employed) cases of these relations are derived, and their assumptions and limitations are discussed. To investigate the utility of these retrieval relations for liquid water content (LWC) and ice water content (IWC) in rain and hail as well as the potential for improvement using polarimetric variables, two models with spectral bin microphysics coupled with a polarimetric radar operator are used: a one-dimensional melting hail model and the two-dimensional Hebrew University Cloud Model. The relationship between LWC and Z in pure rain varies spatially and temporally, with biases clearly seen using the normalized number concentration. Retrievals using Z perform the poorest while specific attenuation and specific differential phase shift (KDP) perform much better. Within rain–hail mixtures, separate estimation of LWC and IWC is necessary. Prohibitively large errors in the retrieved LWC may result when using Z. The quantity KDP can be used to effectively retrieve the LWC and to isolate the contribution of IWC to Z. It is found that the relationship between Z and IWC is a function of radar wavelength, maximum hail diameter, and principally the height below the melting layer, which must be accounted for in order to achieve accurate retrievals.

A comparison of two optical precipitation sensors with different operating principles: The PWS100 and the OAP-2DP

Precipitation measurements can be performed with a variety of techniques and instruments. Optical devices allow for rain measurements in terms of the number and size of individual drops and have been helpful to improve the knowledge of the processes involved in the development and variability of precipitation. A comparative study was conducted during natural rain events in Mexico City using two classes of optical instruments: a light beam occlusion type (OAP-2DP) and a light scatter sensor (PWS100). The sensors were collocated a few metres from each other, along with a tipping bucket rain gauge to simultaneously record rainfall intensity. The results show that both optical instruments measured larger amounts of accumulated rain compared to the rain gauge. Bias and percent bias outcomes suggest that the PWS100 records the largest rain amounts among the devices used, but statistical analyses and the study of instrumental uncertainties did not provide enough evidence to prove significant differences in rain estimation. Although the PWS100 detects larger drops than the OAP-2DP, no significant differences were found between the liquid water content estimates obtained with them. Drop size spectra and velocity distributions also showed good agreement. In particular, both instruments showed similar characteristics for drop fall speed distributions that may be attributed to effects in the hydrometeor fall behaviour produced by horizontal wind. It is concluded that the performance of these two optical instruments is very similar, and that the PWS100 provides reliable data for rain measurements.

A novel sensor combination (upGPR‐GPS) to continuously and nondestructively derive snow cover properties

AbstractMonitoring seasonal snow cover properties is critical for properly managing natural hazards such as snow avalanches or snowmelt floods. However, measurements often cannot be conducted in difficult terrain or lack the high temporal resolution needed to account for rapid changes in the snowpack, e.g., liquid water content (LWC). To monitor essential snowpack properties, we installed an upward looking ground‐penetrating radar (upGPR) and a low‐cost GPS system below the snow cover and observed in parallel its evolution during two winter seasons. Applying external snow height (HS) information, both systems provided consistent LWC estimates in snow, based on independent approaches, namely measurements of travel time and attenuation of electromagnetic waves. By combining upGPR and GPS, we now obtain a self‐contained approach instead of having to rely on external information such as HS. This allows for the first time determining LWC, HS, and snow water equivalent (SWE) nondestructively and continuously potentially also in avalanche‐prone slopes.

Quantification of ice nuclei active at near 0 °C temperatures in low-altitude clouds at the Puy de Dôme atmospheric station

Abstract. The distribution, abundance and nature of ice nucleation active particles in the atmosphere are major sources of uncertainty in the prediction of cloud coverage, precipitation patterns and climate. Some biological ice nuclei (IN) induce freezing at temperatures at which most other atmospheric particles exhibit no detectable activity (> −10 °C). Their actual contribution to the pool of IN in clouds remains poorly known, but numerical studies have suggested a probable significance of biological IN in atmospheric processes. In this study, cloud water was collected aseptically from the summit of Puy de Dôme (1465 m a.s.l., France) within contrasted meteorological and physico-chemical situations. Total and biological (i.e. heat-sensitive) IN were quantified by droplet-freezing assay between −5 °C and −14 °C. We observed that freezing was systematically induced by biological material, between −6 °C and −8 °C in 92% of the samples. Its removal by heat treatment consistently led to a decrease of the onset freezing temperature, by 3 °C or more in most samples. At −10 °C, 0 to ~ 220 biological IN mL−1 of cloud water were measured (i.e. 0 to ~ 22 m−3 of cloud air based on cloud liquid water content estimates), and these represented 65% to 100% of the total IN. Based on back-trajectories and on physico-chemical analyses, the high variability observed resulted probably from a source effect, with IN originating mostly from continental sources. Assuming that biological IN were all bacteria, at maximum 0.6% of the bacterial cells present in cloud water samples could have acted as IN at −8 °C, 1.5% at −10 °C, and 3.1% at −12 °C. The data set generated here will help elucidate the role of biological and bacterial IN on cloud microphysics by numeric modelling, and their impact on precipitation at local scale.

A Multisensor Perspective on the Radiative Impacts of Clouds and Aerosols

AbstractThe launch of CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) in 2006 provided the first opportunity to incorporate information about the vertical distribution of cloud and aerosols directly into global estimates of atmospheric radiative heating. Vertical profiles of radar and lidar backscatter from CloudSat’s Cloud Profiling Radar (CPR) and the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard CALIPSO naturally complement Moderate Resolution Imaging Spectroradiometer (MODIS) radiance measurements, providing a nearly complete depiction of the cloud and aerosol properties that are essential for deriving high-vertical-resolution profiles of longwave (LW) and shortwave (SW) radiative fluxes and heating rates throughout the atmosphere. This study describes a new approach for combining vertical cloud and aerosol information from CloudSat and CALIPSO with MODIS data to assess impacts of clouds and aerosols on top-of-atmosphere (TOA) and surface radiative fluxes. The resulting multisensor cloud–aerosol product is used to document seasonal and annual mean distributions of cloud and aerosol forcing globally from June 2006 through April 2011. Direct comparisons with Clouds and the Earth’s Radiant Energy System (CERES) TOA fluxes exhibit a close correlation, with improved errors relative to CloudSat-only products. Sensitivity studies suggest that remaining uncertainties in SW fluxes are dominated by uncertainties in CloudSat liquid water content estimates and that the largest sources of LW flux uncertainty are prescribed surface temperature and lower-tropospheric humidity. Globally and annually averaged net TOA cloud radiative effect is found to be −18.1 W m−2. The global, annual mean aerosol direct radiative effect is found to be −1.6 ± 0.5 W m−2 (−2.5 ± 0.8 W m−2 if only clear skies over the ocean are considered), which, surprisingly, is more consistent with past modeling studies than with observational estimates that were based on passive sensors.

Field evaluation of a new method for estimation of liquid water content and snow water equivalent of wet snowpacks with GPR

Estimates of snow water equivalent (SWE) with ground-penetrating radar can be used to calibrate and validate measurements of SWE over large areas conducted from satellites and aircrafts. However, such radar estimates typically suffer from low accuracy in wet snowpacks due to a built-in assumption of dry snow. To remedy the problem, we suggest determining liquid water content from path-dependent attenuation. We present the results of a field evaluation of this method which demonstrate that, in a wet snowpack between 0.9 and 3 m deep and with about 5 vol% of liquid water, liquid water content is underestimated by about 50% (on average). Nevertheless, the method decreases the mean error in SWE estimates to 16% compared to 34% when the presence of liquid water in snow is ignored and 31% when SWE is determined directly from two-way travel time and calibrated for manually measured snow density.

Sensitivity of Cloud Liquid Water Content Estimates to the Temperature-Dependent Thermodynamic Phase: A Global Study Using CloudSat Data

Abstract The main purpose of this study is to underline the sensitivity of cloud liquid water content (LWC) estimates purely to 1) the shape of computationally simplified temperature-dependent thermodynamic phase and 2) the range of subzero temperatures covered to partition total cloud condensate into liquid and ice fractions. Linear, quadratic, or sigmoid-shaped functions for subfreezing temperatures (down to −20° or −40°C) are often used in climate models and reanalysis datasets for partitioning total condensate. The global vertical profiles of clouds obtained from CloudSat for the 4-yr period June 2006–May 2010 are used for sensitivity analysis and the quantitative estimates of sensitivities based on these realistic cloud profiles are provided. It is found that three cloud regimes in particular—convective clouds in the tropics, low-level clouds in the northern high latitudes, and middle-level clouds over the midlatitudes and Southern Ocean—are most sensitive to assumptions on thermodynamic phase. In these clouds, the LWC estimates based purely on quadratic or sigmoid-shaped functions with a temperature range down to −20°C can differ by up to 20%–40% over the tropics (in seasonal means), 10%–30% over the midlatitudes, and up to 50% over high latitudes compared to a linear assumption. When the temperature range is extended down to −40°C, LWC estimates in the sigmoid case can be much higher than the above values over high-latitude regions compared to the commonly used case with quadratic dependency down to −20°C. This sensitivity study emphasizes the need to critically investigate radiative impacts of cloud thermodynamic phase assumptions in simplified climate models and reanalysis datasets.

Semi‐volatile aerosols in Beijing (R.P. China): Characterization and influence on various PM2.5 measurements

During summer, the Beijing urban area meets contrasting meteorological conditions including warm and humid monsoon winds from the southeast bringing high levels of pollution, with PM2.5 mass concentrations often exceeding 100 μg/m3. The specific weather conditions and aerosol chemical composition observed at that time offer a unique opportunity to address the question of the contribution of semi‐volatile material (SVM) in the continuous PM records available for the south East Asian urban regions. Different PM2.5 measurements were evaluated during a 3‐week field campaign performed in Beijing downtown during the summer 2004, and consisting of a Rupprecht & Patashnik (R&P TEOM) (heating air sample at 50°C), R&P TEOM‐FDMS (enabling SVM measurement), optical GRIMM counter, and filter weighing. A good agreement was found between the different TEOM measurements during the campaign with the exception of the periods of high Relative Humidity (RH), which exhibited SVM levels (derived from PM2.5 loss in the TEOM heated at 50°C) as high as 140 μg/m3. Continuous artefact‐free PM2.5 nitrate measurements were performed simultaneously and showed a close relationship with the TEOM‐derived SVM, accounting for half of this SVM. To better document the role of RH on SVM, estimates of liquid water content (LWC) in aerosols were derived from light scattering coefficient and integrated aerosol volume measurements performed in the field at different RH. LWC is shown to be related in a quantitative way to the levels of SVM and nitrate in aerosols, and thus is believed to play a major role in the gas‐particle partitioning of semi‐volatile species in Beijing aerosols.

Raman lidar for the study of liquid water and water vapor in the troposphere

A Raman lidar system based on a tripled Nd:YAG laser is used for profiling of water vapor and liquid water in the troposphere. The Raman signals from water in the gas and liquid state are separated by interference filters and their relative intensities are studied for different atmospheric conditions. For clean weather or immediately after the rain the Raman signal from liquid water inside PBL is about one order of magnitude lower than the signal from water vapor. But during cloud measurements both Raman signals become comparable and the results of water vapor measurements must be corrected for the interference of liquid water Raman scattering. The obtained results are used for the estimation of liquid water content in the atmosphere.

Estimating effective radius and liquid water content from radar and lidar based on the CLARE98 data-set

Retrieval methods to estimate the effective radius (re) and the liquid water content (LWC) of stratocumulus clouds from radar and lidar observations are presented here. The retrieval of re is accomplished by combining radar and lidar observations by means of a Z/α − re relation, which is derived from the measurements of in-situ particle probe instruments. The retrieval of LWC, on the other hand, is done solely on the basis of radar observations. However, when plotting a Z-LWC diagram, there was too much scatter, preventing a direct derivation of a Z-LWC relation. It appears that the deviating points in the Z-LWC scatter diagram are mainly caused by the size distributions that have a rather large re. By filtering out these distributions, the scatter in the Z-LWC diagram is significantly reduced and an empirical Z-LWC relation is derived. The estimates of re and LWC using airborne radar and lidar show reasonably good agreements with the profiles measured in-situ. A comparison of the LWC estimated from ground-based radar data, integrated over the cloud height, with the liquid water path provided by a radiometer also shows good z agreement, with a general offset of about 50 g m−2.

Assessment of one-dimensional icing forecast model applied to stratiform clouds

Aircraft icing can seriously impair aircraft performance. In this paper we assess a one-dimensiona l icing forecast model (presently residing at the U.S. Army Atmospheric Sciences Laboratory) by using recent airborne collected microphysics data. Our prime interest was the variation of potential aircraft icing in stratiform clouds. We discuss the icing model microphysics, including algorithms used to determine icing severity indices, temperature, liquid water content (LWC), and median volume diameter (MVD) of supercooled water droplets. Output from the one-dimensional model suggests that the icing potential in stratiform clouds does not exceed light. The model more often forecasts trace icing for both fixed-wing aircraft and helicopters. The model does not compute a value for LWC great enough to allow for more serious icing events. We therefore conclude that a method must be established to obtain better estimates of drop-size characteristics and LWC (for stratus clouds) to forecast the full range of potential icing for Army aircraft. Otherwise when using the one-dimensional model as currently structured, one would not expect any more than light icing when stratus clouds are observed.

Differential reflectivity and circular depolarization ratio radar signals and related drop oscillation and propagation effects in rainfall

Differential reflectivity (ZDR) and circular depolarization ratio (CDR) radar signals are directly related to each other and to the median drop size diameter of rainfall when the drop sizes are exponentially distributed. These polarization parameters are examined for both static and oscillating raindrop shapes along with their relationships with rainfall rate and liquid water content estimates derived from ZDR or CDR and reflectivity factor (Z) measurements. Propagation effects at S band wavelengths are also considered and shown to be particularly important in CDR measurements.

A Statistical Examination of Nimbus-7 SMMR Data and Remote Sensing of Sea Surface Temperature, Liquid Water Content in the Atmosphere and Surface Wind Speed

Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) brightness temperature measurements over the global oceans have been examined with the help of statistical and empirical techniques. Such analyses show that zonal averages of brightness temperature measured by SMMR, over the oceans, on a large scale are primarily influenced by the water vapor in the atmosphere. Liquid water in the clouds and rain, which has a much smaller spatial and temporal scale, contributes substantially to the variability of the SMMR measurements within the latitudinal zones. The surface wind not only increases the surface emissivity but through its interactions with the atmosphere produces correlations, in the SMMR brightness temperature data, that have significant meteorological implications. It is found that a simple meteorological model can explain the general characteristics of the SMMR data. With the help of this model methods to infer over the global oceans, the surface temperature, liquid water content in the atmosphere, and surface wind speed are developed. Monthly mean estimates of the sea surface temperature and surface winds are compared with the ship measurements. Estimates of liquid water content in the atmosphere are consistent with earlier satellite measurements.