Cloud Top Height Research Articles

Fine vertical structures of cloud from a ground‐based cloud radar over the western Tianshan mountains

AbstractIn this study, the grounded‐based Ka‐band millimetre‐wave cloud radar (MMCR) is used to analyse the cloud characteristics over the western Tianshan mountains. The cloud top height (CTH) obtained by MMCR is verified by comparing it with the Fengyun‐4A (FY‐4A) observations. Overall, the MMCR‐obtained CTHs are attenuated and lower than the FY‐4A‐obtained CTHs under precipitation conditions. Thus, the FY‐4A data is used to complement MMCR data when there is rainfall. The diurnal, seasonal variation and vertical structures of the cloud are further examined using the combination of MMCR and FY‐4A. The result indicates that the CTH and cloud base height (CBH) are highest in summer and lowest in winter. Also, clouds tend to form frequently at night in spring, summer, and winter. Although in autumn, clouds tend to form most frequently in the morning. This may be related to the diurnal variation of temperature, humidity, wind speed, and wind direction. Moreover, the CTHs occur most frequently at heights of 8–9 km in spring and autumn, 9–10 km in summer, and 7–8 km in winter. The high CTHs caused by the strong convective activities in summer may be related to sufficient water vapour transport. These corrected CTH data are also used to classify cloud types, the results indicate that the proportion of high clouds is highest in summer, while the proportion of medium clouds is lower than in the other three seasons. Also, the average CTH of the low, medium, and high clouds is highest in summer and lowest in winter.

Estimating Phase Transition Rates in Shallow Cumulus Clouds from Mass Flux. Part I: Theory and Numerical Simulations

Abstract The system of trade wind cumulus clouds observed during the RICO field project was simulated by an LES model in a domain of the size of a mesoscale model grid. More than 2000 clouds were analyzed by stratifying them by their cloud-top heights. The investigation was focused on phase transition rates (TR), which in warm tropical clouds are represented by the processes of condensation/evaporation. We previously demonstrated, based on LES data, that a nearly perfect correlation (R = 0.99) exists between upward mass flux (MFP) and condensation rate (CR), and that the correlation between MFP and evaporation rate (ER) is only slightly lower (R = 0.98). The strong dependence of TR on MFP and the linear relationship between them were explained by applying condensation theory and the concept of “quasi-steady” supersaturation. The LES-derived slope of the linear TR–MFP relationship agreed with its theoretical value, with an error of less than 5%. This result implies that supersaturation in clouds, on average, varies within a few percentage points of its quasi-steady value. In our analysis we considered parameters characterizing cloud as a whole, that is, parameters integrated over the cloud volume. However, condensation theory and LES data show that the linear fit is applicable to local variables and therefore may be integrated to obtain relationships for horizontally averaged variables. Expanding the TR–MFP relationship to vertically dependent variables may provide the framework for development of subgrid-scale latent heat release parameterization. Significance Statement This study investigated condensation/evaporation processes in tropical cumulus clouds. The energy exchanged during these processes is an important driving force behind a wide range of atmospheric phenomena. We found theoretically, and confirmed in computer simulations, that the rate of condensation/evaporation can be expressed as a linear function of the cloud vertical velocity. This finding suggests a new approach to calculate cloud energy transformations in numerical weather prediction models.

Uncertainty-bounded estimates of ash cloud properties using the ORAC algorithm: application to the 2019 Raikoke eruption

Abstract. Uncertainty-bounded satellite retrievals of volcanic ash cloud properties such as ash cloud-top height, effective radius, optical depth and mass loading are needed for the robust quantitative assessment required to warn aviation of potential hazards. Moreover, there is an imperative to improve quantitative ash cloud estimation due to the planned move towards quantitative ash concentration forecasts by the Volcanic Ash Advisory Centers. Here we apply the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm to Advanced Himawari Imager (AHI) measurements of the ash clouds produced by the June 2019 Raikoke (Russia) eruption. The ORAC algorithm uses an optimal estimation technique to consolidate a priori information, satellite measurements and associated uncertainties into uncertainty-bounded estimates of the desired state variables. Using ORAC, we demonstrate several improvements in thermal infrared volcanic ash retrievals applied to broadband imagers. These include an improved treatment of measurement noise, accounting for multi-layer cloud scenarios, distinguishing between heights in the troposphere and stratosphere, and the retrieval of a wider range of effective radii sizes than existing techniques by exploiting information from the 10.4 µm channel. Our results indicate that 0.73 ± 0.40 Tg of very fine ash (radius ≤ 15 µm) was injected into the atmosphere during the main eruptive period from 21 June 18:00 UTC to 22 June 10:00 UTC. The total mass of very fine ash decreased from 0.73 to 0.10 Tg over ∼ 48 h, with an e-folding time of 20 h. We estimate a distal fine ash mass fraction of 0.73 % ± 0.62 % based on the total mass of very fine ash retrieved and the ORAC-derived height–time series. Several distinct ash layers were revealed by the ORAC height retrievals. Generally, ash in the troposphere was composed of larger particles than ash present in the stratosphere. We also find that median ash cloud concentrations fall below peak ash concentration safety limits (< 4 mg m−3) 11–16 h after the eruption begins, if typical ash cloud geometric thicknesses are assumed. The ORAC height retrievals for the near-source plume showed good agreement with GOES-17 side-view height data (R=0.84; bias = −0.75 km); however, a larger negative bias was found when comparing ORAC height retrievals for distal ash clouds against Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP) measurements (R=0.67; bias = −2.67 km). The dataset generated here provides uncertainties at the pixel level for all retrieved variables and could potentially be used for dispersion model validation or be implemented in data assimilation schemes. Future work should focus on improving ash detection, improving height estimation in the stratosphere and exploring the added benefit of visible channels for retrieving effective radius and optical depth in opaque regions of nascent ash plumes.

Robust variation trends in cloud vertical structure observed from three-decade radiosonde record at Lindenberg, Germany

Clouds substantially affect Earth's energy budget by their interactions with short/long-wave radiation, and small changes in cloud vertical structure (CVS, i.e., the locations of cloud base and top, the thickness and number of cloud layers) can fluctuate the warming effects of anthropogenic forcing. However, the knowledge regarding how the CVS changes over time is still poor owing to a lack of reliable and available datasets. Here, the long-term temporal variations in four-times-daily radiosonde-derived CVS at Lindenberg, Germany, from 1992 to 2020 are analyzed. Robust increasing trends in cloud height/thickness and overlap frequency are found during this three-decade period. Observational results show that the interannual variabilities of the cloud-top height, cloud-base height, and cloud thickness are consistently increasing, with linear trends of 137.75 ± 6.65 m yr−1, 45.73 ± 6.22 m yr−1, and 29.52 ± 3.53 m yr−1, respectively. The occurrence of multi-layer cloud generally increases with a trend of 1.57 ± 0.06% yr−1, whereas the percentages of single-layer cloud and cloudless conditions decrease with trends of −0.44 ± 0.05% yr−1 and −1.12 ± 0.07% yr−1, respectively. Continuous surface warming increases thermal instability, which raises cloud height, particularly for single-layer clouds and the lower layer of multi-layer clouds. The upper-level cloud generates and maintains itself as the upper water vapor and vertical velocity enlarge, thus increasing the cloud top height, total cloud thickness, and cloud overlap frequency. These findings shed new light on the long-term changes in cloud properties caused by climate change.

Macro- and microphysical characteristics of snowfall and non-snowfall clouds in the West Tianshan Mountains of China based on cloud radar

The macro- and microphysical characteristics of wintertime precipitating clouds and non-precipitating clouds over the West Tianshan Mountains, China, were analyzed with the use of Ka-band radar and weighing rain gauge observations. The data were collected from January to February 2019, December 2019, and from December 2020 to February 2021. Snowfall clouds mainly ranged from 0.15 ~ 2.50 km and had a reflectivity (Z) of mostly 10 ~ 33 dBZ. Non-snowfall clouds were primarily distributed within the height range of 2 ~ 8 km, and the Z values were within the range of − 22 ~ 15 dBZ. Compared with non-snowfall clouds, snowfall clouds have a higher particle water content (M) but a similar radial velocity (V). Light and moderate snowfall clouds were mainly located at heights of 0.15 ~ 3.50 km and had Z values concentrated from 5 ~ 24 dBZ. Heavy snowfall clouds were characterized by a Z of 5 ~ 30 dBZ below 3.5 km. The proportion of clouds with an M value > 0.1 g·m−3 below 2 km was noticeably higher for heavy snow events than for light and moderate snow events. The differences in the distributions and values of snowfall cloud V values were small among the different snow types, and descending motions occurred below 6 km, with V ranging − 1.4 ~ − 0.3 m·s−1. The heights of the non-snowfall cloud top and base during the day were lower than those at night. The snowfall cloud top did not show noticeable diurnal variations. The cloud top and base heights of the non-snowfall clouds both showed a single-peak distribution. The cloud top values of snowfall clouds exhibited bimodal distributions.

Analysis of Diurnal Evolution of Cloud Properties and Convection Tracking over the South China Coastal Area

Different diurnal rainfall cycles occur over the offshore and inland regions of the South China coastal area (SCCA). Inspired by these findings, in this study, we investigated the diurnal evolution features of cloud systems and cloud properties inside such systems for both the SCCA offshore and inland regions, using cloud data retrieved from a recently developed deep neural network model. Rainy day data for June 2017 revealed that the ice cloud optical thickness and top height reached their peak intensities at noon (~12 local standard time (LST)) over the offshore region, approximately 2 h later than the rainfall peak. Over the inland region, cloud and rainfall peaks simultaneously appeared from ~18 to 20 LST. When further examining the cloud-amount variation of different ice-cloud types, we found a clear diurnal oscillation in the medium-thick cloud amount over the offshore region, while for the inland region, this cloud type had no obvious diurnal peak, showing a low cloud amount throughout the day. This phenomenon suggests different inner structures and intensities between offshore and inland convections. To better elucidate the convection features over different regions, a tracking algorithm was applied to obtain various parameters, such as size, number, and duration of mesoscale convective systems. The strongest convections, which lasted over 12 h, tended to be abundant over the offshore region from ~03 to 12 LST, and an inland to offshore migration at ~03 LST was facilitated by the beneficial meteorological conditions observed at 113–116˚E, 20.5–22.5˚N.

Effects of Different Types of Aerosols on Deep Convective Clouds and Anvil Cirrus

AbstractDeep convective clouds and associated anvils exert opposite radiative effects. The impact of different aerosol types on these two categories of clouds remains a major challenge in understanding aerosol‐cloud interactions. Using 11‐year satellite retrievals, we find that cloud top height (CTH) and ice cloud fraction of deep convective clouds and anvil cirrus identified by Cloud‐Aerosol Lidar with Orthogonal Polarization increase with small aerosol loadings and level off or even decrease with further aerosol increase. Compared with continental aerosols, CTH affected by marine aerosols starts to decrease at smaller aerosol loadings. Moreover, cloud optical depth (COD) of deep convective clouds decreases with aerosol loadings. COD of anvil cirrus increases with increased loadings of most aerosol types but decreases with smoke aerosol. These relationships are mainly attributed to the aerosol effect rather than the meteorological effects. Our findings contribute to the development of models and better assessment of aerosol‐cloud radiative forcing.

Distinct Dynamical and Structural Properties of Marine Stratocumulus and Shallow Cumulus Clouds in the Eastern North Atlantic

AbstractLow‐level clouds in a marine environment are examined using multiple ground‐based instruments. We used Doppler radar and Doppler lidar measurements (a) to examine cloud properties including cloud base and top height and thickness and (b) to investigate vertical velocity (w) statistics throughout the subcloud and in‐cloud layers. Marine stratocumulus (Sc) and shallow cumulus (Cu) clouds have distinct properties in their horizontal and vertical extent. We also find that the two cloud types have different thermodynamic (coupled and decoupled conditions) and dynamical characteristics for the first four moments of the w distributions (mean, variance, skewness, and kurtosis). We identify a strong dependence of subcloud mean w on near‐surface horizontal wind direction, implying that topographic effects exert a substantial impact on boundary layer dynamics and cloud properties at Graciosa Island. Our findings strongly suggest that care must be taken when interpreting island‐based observations as representative of a generic marine dynamical environment. This information also can be useful for guiding and constraining the future global model development using an observational data set.

Lightning-induced chemistry on tidally-locked Earth-like exoplanets

ABSTRACT Determining the habitability and interpreting atmospheric spectra of exoplanets requires understanding their atmospheric physics and chemistry. We use a 3-D coupled climate-chemistry model, the Met Office Unified Model with the UK Chemistry and Aerosols framework, to study the emergence of lightning and its chemical impact on tidally-locked Earth-like exoplanets. We simulate the atmosphere of Proxima Centauri b orbiting in the Habitable Zone of its M-dwarf star, but the results apply to similar M-dwarf orbiting planets. Our chemical network includes the Chapman ozone reactions and hydrogen oxide (HOx = H + OH + HO2) and nitrogen oxide (NOx = NO + NO2) catalytic cycles. We find that photochemistry driven by stellar radiation (177–850 nm) supports a global ozone layer between 20–50 km. We parametrize lightning flashes as a function of cloud-top height and the resulting production of nitric oxide (NO) from the thermal decomposition of N2 and O2. Rapid dayside convection over and around the substellar point results in lightning flash rates of up to 0.16 flashes km−2 yr−1, enriching the dayside atmosphere below altitudes of 20 km in NOx. Changes in dayside ozone are determined mainly by UV irradiance and the HOx catalytic cycle. ∼45 per cent of the planetary dayside surface remains at habitable temperatures (Tsurf > 273.15K), and the ozone layer reduces surface UV radiation levels to 15 per cent. Dayside–nightside thermal gradients result in strong winds that subsequently advect NOx towards the nightside, where the absence of photochemistry allows NOx chemistry to involve reservoir species. Our study also emphasizes the need for accurate UV stellar spectra to understand the atmospheric chemistry of exoplanets.

Investigation of the Convection‐Allowing Prediction Error of an Extreme Precipitation Event of China Using CRTM‐Simulated Brightness Temperature

AbstractAn extreme rainfall case occurred on 20 July 2016 in northern China, producing over 600 mm of maximum 24 hr accumulated rainfall. The case is characterized by low‐echo centroid (LEC) based on radar observations. It is simulated by Weather Research and Forecasting model at 4 km grid spacing using the Morrison two‐moment microphysics scheme. Infrared brightness temperature (BT) simulated using a radiative transfer model together with simulated reflectivity are used to evaluate the simulated cloud structures. In general, the model predicts the rainfall amount, location, and propagation well. However, it fails to accurately predict the three‐dimensional cloud structures. The predicted convective cores (>35 dBZ) are higher than the freezing level, suggesting the presence of active cold‐cloud processes while the observed LEC suggests that warm‐cloud processes dominate. The simulation also produces too many upper level clouds and over‐predicts the fractions of overshooting clouds, resulting great over‐prediction of cloud top height (CTH). Additionally, the sensitivity of simulated BT to predicted cloud properties including CTH, cloud species and hydrometeor effective radii are examined to better understand the sources of error in simulated BT. Among those factors, CTH is found to be most critical to BT simulation. For every kilometer of CTH over‐prediction, there is about 6.431 K of negative BT bias owing to the lapse rate and the absorption and scattering effects of cloud particles. For most cloudy regions, cloud ice dominates the effect on simulated BT. Using diagnosed effective radii of simulated hydrometeors within the radiative transfer model results in small improvement to the BT simulation.

Characteristics of Latent Heating Rate From GPM and Convective Gravity Wave Momentum Flux Calculated Using the GPM Data

AbstractParameterizations of convective gravity‐wave (CGW) drag (CGWD) require cloud information as input parameters. As cloud information provided from reanalyses includes some uncertainties, observed cloud information is required for better representation of CGWs. For this, characteristics of the latent heating rate (LHR) based on the Global Precipitation Measurement (GPM) satellite over 6 yr (June 2014 to May 2020) are investigated, and the CGW momentum flux and CGWD based on an offline CGWD parameterization are calculated using the GPM‐LHR and the Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) background variables. Additionally, they are compared with those using LHR afforded by MERRA‐2. The averaged cloud‐bottom height is lower than that from MERRA but the cloud top height is similar for the both data, yielding deeper clouds from GPM that can generate more high phase‐speed components of CGWs. The column‐maximum heating rate, which is an input of the CGW momentum flux, of GPM‐LHR is maximal near the equator and the secondary maximum locates in the winter hemisphere storm tracks. The maximum of the cloud top momentum flux (CTMF) of CGWs locates in the winter hemisphere storm tracks, with the GPM‐CTMF being much larger than MERRA‐CTMF, as extreme convective events occur more frequently in GPM. In the equatorial region above z = 40 km, the GPM‐CGWD is significantly larger because high phase‐speed components of CGWs that survive up to the upper stratosphere are abundant for GPM‐CTMF, and this will contribute to drive more realistic semi‐annual oscillation.

Confidence and Error Analyses of the Radiosonde and Ka-Wavelength Cloud Radar for Detecting the Cloud Vertical Structure

A macro-vertical structure is closely related to weather evolution and the energy budget balance of the atmospheric system of the Earth. In this study, radiosonde data were used to identify a cloud vertical structure (CVS) using the adjusted relative humidity threshold method. To evaluate the reliability and stability of this method, the results obtained based on the spatiotemporal matching criteria established in this study were compared with Ka-band millimetre-wave cloud radar (MMCR) observation data. This comparison showed that both devices exhibit high consistency in low-level cloud detection. With the increase in the cloud height, the frequency of the cloud appearance detection by the radiosonde became higher than that by the MMCR. In spring, the results of the CVS detection by the two devices were in good agreement. Specifically, the determination coefficients of the modified degrees of freedom (adjusted R-square) of the cloud base height (CBH) and cloud top height (CTH) detected by the two devices were 0.934 and 0.879, respectively. The horizontal drift of the radiosonde was the smallest in summer, and the adj. R-square values of the CBH and CTH were 0.814 and 0.852, respectively. The CVS observation results by the radiosonde and the MMCR were significantly different in autumn (the adj. R-Square values of the CBH and CTH were 0.715 and 0.629, respectively). In winter, the adj. R-Square values of the CBH and CTH observed by the radiosonde and the MMCR were 0.958 and 0.710, respectively. The statistics and analysis of the results of the distribution characteristics of the CVSs using radiosonde data from 2019 to 2021 from Xi’an showed that the average CTH and CBH were at 7–10 km and 3–5 km, respectively. The frequencies of the cloud absence, rainfall, and two- and three-layer clouds were the highest in the winter (34.36%), autumn (12.99%), and summer, respectively.

Physical and Optical Properties of Clouds in the Southwest Vortex from FY-4A Cloud Retrievals

Abstract The southwest vortex (SWV) is a critical weather system in China, but our knowledge of this system remains incomplete. Here, we investigate the cloud properties in the SWV. First, we search for the SWVs with time steps and center locations that are consistent between the SWV yearbook and ERA-Interim reanalysis data. Second, we supplement these SWVs’ life spans and movement paths. Third, we relocate the Fengyun (FY) satellite FY-4A cloud retrievals in the 10° × 10° region centered on each SWV and analyze the cloud occurrence frequency (COF), cloud-top height (CTH), and cloud optical thickness (COT). A distribution mode of cloud types is summarized from the COFs, with water clouds, supercooled clouds, mixed clouds, ice clouds, cirrus clouds, and overlap clouds occurring sequentially from west to east. The CTH probability density (PD) distribution features a significant north–south difference. In addition, the COT PD distributions exhibit a common trend: with increasing COT, the PD increases rapidly and then slowly before peaking, whereupon the PD decreases abruptly. From spring to summer, the region with the highest convective COF shifts from the northeast to the northwest, and an east–west gradient of the convective COF appears in autumn and winter. Furthermore, we investigate the cloud properties during SWV-related heavy rainfall. Heavy rain occurs mainly in the west of the SWV, and convective clouds are mainly in the northwest, partly in the southwest and near the SWV center. The average CTH in heavy rainfall is generally higher than 6 km, and the average COT is greater than 20. Significance Statement The southwest vortex (SWV) is an important weather system in China. However, we do not yet comprehensively know this weather system. The cloud properties can indicate the structures of weather systems and are key parameters in numerical weather prediction (NWP) models. Thus, investigating cloud properties is necessary and meaningful to understand the SWV and accurately predict SWV-related precipitation in NWP models. In this paper, a typical distribution mode of six cloud types in the SWV is summarized from the cloud occurrence frequency, and the distribution features of convective clouds, cloud-top height, and cloud optical thickness in the SWV are analyzed. Furthermore, the cloud properties in SWV-related heavy rain are also studied.

A decadal assessment of the climatology of aerosol and cloud properties over South Africa

Abstract. Aerosol–cloud interactions (ACI) play an essential role in understanding precipitation occurrence and climate change but remains poorly understood. Conducting a climatology study on a regional or global scale constitutes a prospect better to understand ACI and their influence on precipitation and climate. This study analysed the characteristics of ACI over South Africa based on two instruments: the moderate resolution imaging spectroradiometer (MODIS) and the multiangle imaging spectroradiometer (MISR) onboard the Terra satellite, and ground-based meteorology data from South Africa Weather Service (SAWS) between 2007–2016. The region mainly splits into the upper, central, and lower sub-regions based on the aerosol loading characteristics. Findings from the study show that depending on the atmospheric conditions, aerosol exhibits dual features of increasing and decreasing the potential formation of precipitating clouds. However, more often, fine-mode predominated aerosols suppress rain-bearing clouds. Furthermore, the cloud top height (CTH) demonstrates an upward increment from the lower to the upper part of the region, and the cloud fraction (CF) is in the downward direction. Both the CF and CTH display the characteristic enhancers of the precipitation intensity, mainly when the initial conditions necessary for rain bearing occurs. Besides, the cloud optical depth (COD) depends significantly on the liquid water path (LWP) and is suggestively associated with the aerosol–vapour ratio ingested into the cloud. Also notably, the temperature over the entire region has generally increased steadily and continuously from 2013.

Preliminary Application of a Multi-Physical Ensemble Transform Kalman Filter in Cloud and Precipitation Forecasts

In this study, based on the retrieval data from the Fengyun geostationary meteorological satellite and the Tropical Rainfall Measuring Mission satellite, a large-scale precipitation case in eastern China is selected to address the systematic deviations of deterministic forecasts for clouds and precipitation. A multi-physical ensemble transform Kalman filter (ETKF) is constructed in this research based on the Weather Research and Forecast model version 3.6, and its forecasting ability in terms of cloud-top height and temperature, hydrometeors, and precipitation is evaluated by quantitatively comparing three microphysical parameterization schemes (Lin, Morrison, and CAM5.1 schemes) and their corresponding multi-physical ensemble mean. The results show that the Lin, Morrison, and CAM5.1 schemes all underestimate the range of cloud systems and have different advantages and disadvantages in forecasting different elements, while the forecasting improvement of the multi-physical ensemble mean is limited. However, the multi-physical ETKF can effectively improve the forecast accuracy of the cloud system range. In addition, the multi-physical ETKF has the advantages of different physical parameterization schemes, which can dramatically improve the forecast accuracy of cloud hydrometeors, reduce precipitation forecast errors, and improve threat scores.

Electric Field Variations Caused by Low, Middle and High-Altitude Clouds over the Negev Desert, Israel

Ground-based measurements of the electric field from a station located in the arid Negev region of southern Israel have been conducted continuously since 2013. We present here results of observations of the electric field (Potential Gradient, PG) variability during 22 cloudy days, with varying cloud types and cloud base heights, and compare the measured values with the mean fair-weather PG. The results show an increase of PG (~+10 to +70 V m−1) from mean fair weather values during times of low clouds. During times of mid-altitude (alto) clouds or during a superposition of low and high clouds, there were small departures in the PG values (~0 to −30 V m−1) compared to mean fair weather PG values. During times of high-altitude cirrus clouds there is a clear decrease of the PG (~−40 to −90 V m−1). The data was compared with the Israeli meteorological service cloud data and with MODIS 7 satellite cloud top height maps. In addition, AERONET aerosol optical depth values and wind speed magnitude from a local meteorological station were analyzed.

Evaluating WRF-Solar EPS cloud mask forecast using the NSRDB

Improving the accuracy of day-ahead solar forecasts using numerical weather prediction models requires improving the forecasting of cloud occurrence and properties. Validating cloud forecasts is challenging because this requires evaluating different types of clouds over a wide variety of regions. This study analyzes the cloud occurrence (or cloud mask) over the contiguous United States (CONUS), predicted by the Weather Research and Forecasting-Solar Ensemble Prediction System (WRF-Solar EPS), to identify the strengths and limitations of the model in reproducing cloud fields. To enable the in-depth analysis of cloud mask forecasts covering CONUS, we use satellite observations from the National Solar Radiation Database (NSRDB). Two evaluation methods are implemented to consider all clouds and partially removed clouds from the 2-km NSRDB in evaluating the 9-km WRF-Solar EPS cloud mask. Cloud detection metrics as well as the frequency of cloud occurrence are used to quantify the monthly performance of WRF-Solar EPS. Mismatched cloud frequency (MCF) is used to assess the model’s capability to predict different types of clouds, which are classified using three levels of cloud optical depth (COD) and cloud top height (CTH). The day-ahead forecasts covering the full year of 2018 demonstrate that WRF-Solar EPS produces MCFs ranging from 27%–46%, 13%–34%, and 8%–19% for thin, mid-thickness, and thick clouds, respectively. For three CTH levels, the model shows MCFs ranging from 19%–46%, 16%–33%, and 8%–27% for low-level, middle-level, and high-level clouds, respectively. This comprehensive characterization of model performance helps identify model weakness and will eventually lead to improvements in cloud and solar radiation forecasting.

Exploring relations between cloud morphology, cloud phase, and cloud radiative properties in Southern Ocean's stratocumulus clouds

Abstract. Marine stratocumuli are the most dominant cloud type by area coverage in the Southern Ocean (SO). They can be divided into different self-organized cellular morphological regimes known as open and closed mesoscale-cellular convective (MCC) clouds. Open and closed cells are the two most frequent types of organizational regimes in the SO. Using the liDAR-raDAR (DARDAR) version 2 retrievals, we quantify 59 % of all MCC clouds in this region as mixed-phase clouds (MPCs) during a 4-year time period from 2007 to 2010. The net radiative effect of SO MCC clouds is governed by changes in cloud albedo. Both cloud morphology and phase have previously been shown to impact cloud albedo individually, but their interactions and their combined impact on cloud albedo remain unclear. Here, we investigate the relationships between cloud phase, organizational patterns, and their differences regarding their cloud radiative properties in the SO. The mixed-phase fraction, which is defined as the number of MPCs divided by the sum of MPC and supercooled liquid cloud (SLC) pixels, of all MCC clouds at a given cloud-top temperature (CTT) varies considerably between austral summer and winter. We further find that seasonal changes in cloud phase at a given CTT across all latitudes are largely independent of cloud morphology and are thus seemingly constrained by other external factors. Overall, our results show a stronger dependence of cloud phase on cloud-top height (CTH) than CTT for clouds below 2.5 km in altitude. Preconditioning through ice-phase processes in MPCs has been observed to accelerate individual closed-to-open cell transitions in extratropical stratocumuli. The hypothesis of preconditioning has been further substantiated in large-eddy simulations of open and closed MPCs. In this study, we do not find preconditioning to primarily impact climatological cloud morphology statistics in the SO. Meanwhile, in-cloud albedo analysis reveals stronger changes in open and closed cell albedo in SLCs than in MPCs. In particular, few optically thick (cloud optical thickness >10) open cell stratocumuli are characterized as ice-free SLCs. These differences in in-cloud albedo are found to alter the cloud radiative effect in the SO by 21 to 39 W m−2 depending on season and cloud phase.

Using an Ensemble Filter to Improve the Representation of Temporal Source Variations in a Volcanic Ash Forecasting System

The use of ensemble models to forecast the dispersion and transport of airborne volcanic ash in operational contexts is increasingly being explored. The ensemble members are usually constructed to represent a priori uncertainty estimates in meteorological fields and volcanic ash source parameters. Satellite data can be used to further filter ensemble members within an analysis time window by rejecting poorly performing members, leading to improved forecasts. In this study, the ensemble filtering technique is used to improve the representation of temporal source variations. Ensemble members are initially created by representing the source time variations as random functions of time that are modulated by crude initial estimates of the variations estimated from satellite imagery. Ensemble filtering is then used to remove members whose fields match poorly with observations within a specified analysis time window that are represented by satellite retrievals of volcanic ash properties such as mass load, effective radius, and cloud top height. The filtering process leads to an ensemble with statistics in closer agreement with the observations. It is shown in the context of the 30 May 2014 Sangeang Api eruption case study that this method leads to significantly enhanced forecasting skill beyond the analysis time window—about 20% improvement on average—when compared to a system that assumes constant emission rates for the duration of the eruption, as is the case in many operational volcanic ash forecasting systems.

Errors in global cloud climatology due to transect sampling with the CALIPSO satellite lidar mission

Although cloud profiling lidars provide the most accurate data on cloud amount (CA), cloud top height (CTH) and cloud optical thickness (COT), they only sample cloud fields along a one-dimensional transect. This approach introduces uncertainty into studies that interpret cloud layers as two- or three-dimensional. The present study verified the accuracy of mean annual CA, COT, and CTH when calculated based on transect sampling, and when spatiotemporally averaged over grid cells measuring 1°, 2.5°, and 5° in latitude and longitude. The study evaluates the sampling scheme used by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar mission. In this study for the very first time, transect observations are investigated globally, at the level of climatological means, and for actual cloud regimes. Lidar data were simulated based on MODIS observations for 2005, resulting in 3,762,678 individual laser transects, and 58,647 annual means for each parameter under study. The results of this study demonstrate that, for most locations on Earth, CALIPSO sampling does report ‘true’ mean annual CA, COT, and CTH. At 1° resolution, statistically significant (α ≤ 0.05) differences in CA were only noted for 11% of cells, and for 2–3% of cells in the case of COT and CTH. Polar regions were identified as the most affected by transect sampling. Mean global errors (RMSE) observed at the annual timescale were within or close to the accuracy expected for satellite data used in climate change studies: 1.9% (CA), 2.4% (COT), and 187 m (CTH). This study also found that the spatial resolution of the grid impacts the magnitude of errors: an increase in cell size increased the underestimation of cloud parameters, increased the extent of areas where statistically significant errors/ differences occurred, and increased uncertainty in the climatological mean estimation (wider confidence intervals). However, at the cell level, errors were up to ±5% (CA), ±25% (COT) and ± 400 m (CTH); moreover, the magnitude of error was difficult to estimate a priori due to the noisy spatial distribution. These findings suggest that a dedicated uncertainty analysis should be made a requirement in CALIPSO lidar-based studies at fine spatial resolution.