scholarly journals Investigation of Low-Cloud Characteristics Using Mesoscale Numerical Model Data for Improvement of Fog-Detection Performance by Satellite Remote Sensing

2014 ◽  
Vol 53 (10) ◽  
pp. 2246-2263 ◽  
Author(s):  
Haruma Ishida ◽  
Kentaro Miura ◽  
Teruaki Matsuda ◽  
Kakuji Ogawara ◽  
Azumi Goto ◽  
...  
Keyword(s):  
Mesoscale Model ◽  
Potential Temperature ◽  
Vertical Gradient ◽  
Meteorological Conditions ◽  
Lapse Rate ◽  
Cloud Detection ◽  
Ancillary Data ◽  
Low Clouds ◽  
Lower Cloud ◽  
Low Cloud

AbstractThe comprehensive relationship between meteorological conditions and whether low water cloud touches the surface, particularly at sea, is examined with the goal of improving low-cloud detection by satellite. Gridpoint-value data provided by an operational mesoscale model with integration of Multifunction Transport Satellite-2 data can provide sufficient data for statistical analyses to find general parameters that can discern whether low clouds touch the surface, compensating for uncertainty due to the scarcity of observation sites at sea and the infrequent incidence of fog. The analyses reveal that surface-touching low clouds tend to have lower cloud-top heights than those not touching the surface, although the frequency distribution of cloud-top height differs by season. The bottom of the Γ > Γm layer (where Γ and Γm are the vertical gradient and the moist-adiabatic lapse rate of the potential temperature, respectively) with surface-touching low-cloud layers tends to be very low or almost attached to the surface. In contrast, the tops of low-cloud layers not touching the surface tend to occur near the bottom of the Γ > Γm layer. Mechanisms to correlate these meteorological conditions with whether low clouds touch the surface are inferred from investigations into the vertical structure of equivalent potential temperature. These results indicate that the temperature difference between cloud-top height and the surface can be an appropriate parameter to infer whether low clouds touch the surface. It is also suggested that only a little addition of meteorological ancillary data, such as the forecast sea surface temperature, to satellite data allows successful performance of the discrimination.

On the Relationship between Stratiform Low Cloud Cover and Lower-Tropospheric Stability

2006 ◽  
Vol 19 (24) ◽  
pp. 6425-6432 ◽  
Author(s):  
Robert Wood ◽  
Christopher S. Bretherton
Keyword(s):  
Boundary Layer ◽  
Temperature Profile ◽  
Cloud Cover ◽  
Cloud Amount ◽  
Lapse Rate ◽  
Stratus Cloud ◽  
Low Clouds ◽  
Wide Range ◽  
Low Cloud ◽  
Low Cloud Cover

Abstract Observations in subtropical regions show that stratiform low cloud cover is well correlated with the lower-troposphere stability (LTS), defined as the difference in potential temperature θ between the 700-hPa level and the surface. The LTS can be regarded as a measure of the strength of the inversion that caps the planetary boundary layer (PBL). A stronger inversion is more effective at trapping moisture within the marine boundary layer (MBL), permitting greater cloud cover. This paper presents a new formulation, called the estimated inversion strength (EIS), to estimate the strength of the PBL inversion given the temperatures at 700 hPa and at the surface. The EIS accounts for the general observation that the free-tropospheric temperature profile is often close to a moist adiabat and its lapse rate is strongly temperature dependent. Therefore, for a given LTS, the EIS is greater at colder temperatures. It is demonstrated that while the seasonal cycles of LTS and low cloud cover fraction (CF) are strongly correlated in many regions, no single relationship between LTS and CF can be found that encompasses the wide range of temperatures occurring in the Tropics, subtropics, and midlatitudes. However, a single linear relationship between CF and EIS explains 83% of the regional/seasonal variance in stratus cloud amount, suggesting that EIS is a more regime-independent predictor of stratus cloud amount than is LTS under a wide range of climatological conditions. The result has some potentially important implications for how low clouds might behave in a changed climate. In contrast to Miller’s thermostat hypothesis that a reduction in the lapse rate (Clausius–Clapeyron) will lead to increased LTS and increased tropical low cloud cover in a warmer climate, the results here suggest that low clouds may be much less sensitive to changes in the temperature profile if the vertical profile of tropospheric warming follows a moist adiabat.

scholarly journals Albedo susceptibility of Northeastern Pacific stratocumulus: the role of covarying meteorological conditions

2021 ◽  
Author(s):  
Jianhao Zhang ◽  
Xiaoli Zhou ◽  
Graham Feingold
Keyword(s):  
Large Scale ◽  
Meteorological Factors ◽  
Meteorological Conditions ◽  
Radiative Properties ◽  
Liquid Water Path ◽  
Cloud Albedo ◽  
Low Clouds ◽  
Low Cloud ◽  
Ne Pacific

Abstract. Quantification of the radiative adjustment of marine low-clouds to aerosol perturbations, regionally and globally, remains the largest source of uncertainty in assessing current and future climate. An important step towards quantifying the role of aerosol in modifying cloud radiative properties is to quantify the susceptibility of cloud albedo and liquid water path (LWP) to perturbations in cloud droplet number concentration (Nd). We use 10 years of space-borne observations from the polar-orbiting Aqua satellite, to quantify the albedo susceptibility of marine low-clouds over the northeast (NE) Pacific stratocumulus region to Nd perturbations. Overall, we find a low-cloud brightening potential of 20.8 ± 0.96 W m−2 ln(Nd)−1, despite an overall negative LWP adjustment for non-precipitating marine stratocumulus, owing to the high occurrence (37% of the time) of thin non-precipitating clouds (LWP < 55 g m−2) that exhibit brightening. In addition, we identify two more susceptibility regimes, the entrainment-darkening regime (36% of the time), corresponding to negative LWP adjustment, and the precipitating-brightening regime (22% of the time), corresponding to precipitation suppression. The influence of large-scale meteorological conditions, obtained from the ERA5 reanalysis, on the albedo susceptibility is also examined. Over the NE Pacific, clear seasonal covariabilities among meteorological factors related to the large-scale circulation are found to play an important role in grouping favorable conditions for each susceptibility regime. Our results indicate that, for the NE Pacific stratocumulus deck, the strongest positively susceptible cloud states occur most frequently for low cloud top height (CTH), the highest lower-tropospheric stability (LTS), low sea-surface temperature (SST), and the lowest free-tropospheric relative humidity (RHft) conditions, whereas cloud states that exhibit negative LWP adjustment occur most frequently under high CTH and intermediate LTS, SST, and RHft conditions. The warm rain suppression driven cloud brightening is found to preferably occur either under unstable atmospheric conditions (low LTS) or high RHft conditions that co-occur with warm SST. Mutual information analyses reveal a dominating control of LWP, Nd and CTH (cloud state indicators) on low-cloud albedo susceptibility, rather than of the meteorological factors that drive these cloud states.

Low Cloud Detection in Multilayer Scenes using Satellite Imagery with Machine Learning Methods

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Infrared Imaging ◽  
Weather Forecasting ◽  
Auxiliary Information ◽  
Probability Of Detection ◽  
Cloud Detection ◽  
Brightness Temperatures ◽  
Low Clouds ◽  
Low Cloud ◽  
Satellite Sensors

Abstract The detection of multilayer clouds in the atmosphere can be particularly challenging from passive visible and infrared imaging radiometers since cloud boundary information is limited primarily to the topmost cloud layer. Yet detection of low clouds in the atmosphere is important for a number of applications, including aviation nowcasting and general weather forecasting. In this work, we develop pixel-based machine learning-based methods of detecting low clouds, with a focus on improving detection in multilayer cloud situations and specific attention given to improving the Cloud Cover Layers (CCL) product, which assigns cloudiness in a scene into vertical bins. The Random Forest (RF) and Neural Network (NN) implementations use inputs from a variety of sources, including GOES Advanced Baseline Imager (ABI) visible radiances, infrared brightness temperatures, auxiliary information about the underlying surface, and relative humidity (which holds some utility as a cloud proxy). Training and independent validation enlists near-global, actively-sensed cloud boundaries from the radar and lidar systems onboard the CloudSat and CALIPSO satellites. We find that the RF and NN models have similar performances. The probability of detection (PoD) of low cloud increases from 0.685 to 0.815 when using the RF technique instead of the CCL methodology, while the false alarm ratio decreases. The improved PoD of low cloud is particularly notable for scenes that appear to be cirrus from an ABI perspective, increasing from 0.183 to 0.686. Various extensions of the model are discussed, including a nighttime-only algorithm and expansion to other satellite sensors.

A Diagnostic Equation for Tendency of Lapse-Rate-Tropopause Heights and Its Application

2019 ◽  
Vol 76 (11) ◽  
pp. 3337-3350
Author(s):  
Masashi Kohma ◽  
Kaoru Sato
Keyword(s):  
Radio Occultation ◽  
Potential Temperature ◽  
Vertical Gradient ◽  
Reanalysis Data ◽  
Lapse Rate ◽  
Synoptic Scale ◽  
Temperature Lapse Rate ◽  
Planetary Scale ◽  
Baroclinic Waves ◽  
Occultation Data

Abstract The tropopause is the boundary between the troposphere and stratosphere and is normally defined by the temperature lapse rate. Previous studies have noted that synoptic-scale and planetary-scale disturbances bring about lapse-rate-tropopause (LRT) height fluctuations on time scales from several days to several years. In the present study, a diagnostic expression for the tendency of LRT height is derived by assuming that the LRT can be characterized as a discontinuity in the vertical gradient of the potential temperature. In addition, the contribution from each term in the thermodynamic equation to the LRT height is quantified. The derived equation is validated by examining the time variation of the LRT height associated with baroclinic waves in an idealized numerical calculation, that of the zonal-mean LRT height in GPS radio occultation data, and that of the LRT height in reanalysis data.

scholarly journals Low clouds suppress Arctic air formation and amplify high-latitude continental winter warming

2015 ◽  
Vol 112 (37) ◽  
pp. 11490-11495 ◽  
Author(s):  
Timothy W. Cronin ◽  
Eli Tziperman
Keyword(s):  
Air Temperature ◽  
High Latitude ◽  
Climate Model ◽  
Lower Troposphere ◽  
Surface Air Temperature ◽  
Cloud Feedback ◽  
Lapse Rate ◽  
Initial State ◽  
Low Clouds ◽  
Low Cloud

High-latitude continents have warmed much more rapidly in recent decades than the rest of the globe, especially in winter, and the maintenance of warm, frost-free conditions in continental interiors in winter has been a long-standing problem of past equable climates. We use an idealized single-column atmospheric model across a range of conditions to study the polar night process of air mass transformation from high-latitude maritime air, with a prescribed initial temperature profile, to much colder high-latitude continental air. We find that a low-cloud feedback—consisting of a robust increase in the duration of optically thick liquid clouds with warming of the initial state—slows radiative cooling of the surface and amplifies continental warming. This low-cloud feedback increases the continental surface air temperature by roughly two degrees for each degree increase of the initial maritime surface air temperature, effectively suppressing Arctic air formation. The time it takes for the surface air temperature to drop below freezing increases nonlinearly to ∼10 d for initial maritime surface air temperatures of 20 °C. These results, supplemented by an analysis of Coupled Model Intercomparison Project phase 5 climate model runs that shows large increases in cloud water path and surface cloud longwave forcing in warmer climates, suggest that the “lapse rate feedback” in simulations of anthropogenic climate change may be related to the influence of low clouds on the stratification of the lower troposphere. The results also indicate that optically thick stratus cloud decks could help to maintain frost-free winter continental interiors in equable climates.

scholarly journals Atypical Thermodynamic Profiles in Hurricanes

2008 ◽  
Vol 136 (2) ◽  
pp. 631-643 ◽  
Author(s):  
Gary M. Barnes
Keyword(s):  
Surface Layer ◽  
Energy Content ◽  
Potential Temperature ◽  
Lapse Rate ◽  
Equivalent Potential Temperature ◽  
Wind Speeds ◽  
Equivalent Potential ◽  
Lower Cloud ◽  
Unstable Layer ◽  
Negative Impacts

Abstract The global positioning system dropwindsondes deployed in Hurricane Bonnie on 26 August 1998 with supporting deployments in Hurricanes Mitch (1998) and Humberto (2001) are used to identify three unusual thermodynamic structures in the lower-cloud and subcloud layers. Two of these structures impact the energy content of the inflow and therefore the intensity of the hurricane. First, positive lapse rates of equivalent potential temperature are found near the top of the inflow. These layers insulate the inflow from the negative impacts of entrainment mixing and promote rapid energy increases, especially near the eyewall. The second structure is a rapid decrease of equivalent potential temperature adjacent to the sea surface. This is essentially a prominent surface layer that owes its existence to both higher moisture content and a superadiabatic lapse rate. The steep lapse rate most often occurs under and near the eyewall where wind speeds at the surface exceed hurricane force. The author speculates that water loading from spray increases the residence time of air parcels in the surface layer, contributing to the creation of this structure. The third feature is a moist absolutely unstable layer previously identified by Bryan and Fritsch for the midlatitudes. These layers are found adjacent to the eyewall, in rainbands, and in the hub cloud within the eye and are evidence of mesoscale or vortex-scale convergence and the very modest instabilities often found in the core of a hurricane.

scholarly journals A New Methodology for Estimating the Surface Temperature Lapse Rate Based on Grid Data and Its Application in China

2018 ◽  
Vol 10 (10) ◽  
pp. 1617 ◽  
Author(s):  
Yun Qin ◽  
Guoyu Ren ◽  
Tianlin Zhai ◽  
Panfeng Zhang ◽  
Kangmin Wen
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
North China ◽  
Seasonal Pattern ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Vertical Gradient ◽  
Lapse Rate ◽  
Grid Data ◽  
Temperature Lapse Rate

Land surface temperature (LST) is an important parameter in the study of the physical processes of land surface. Understanding the surface temperature lapse rate (TLR) can help to reveal the characteristics of mountainous climates and regional climate change. A methodology was developed to calculate and analyze land-surface TLR in China based on grid datasets of MODIS LST and digital elevation model (DEM), with a formula derived on the basis of the analysis of the temperature field and the height field, an image enhancement technique used to calculate gradient, and the fuzzy c-means (FCM) clustering applied to identify the seasonal pattern of the TLR. The results of the analysis through the methodology showed that surface temperature vertical gradient inversion widely occurred in Northeast, Northwest, and North China in winter, especially in the Xinjiang Autonomous Region, the northern and the western parts of the Greater Khingan Mountains, the Lesser Khingan Mountains, and the northern area of Northwest and North China. Summer generally witnessed the steepest TLR among the four seasons. The eastern Tibetan Plateau showed a distinctive seasonal pattern, where the steepest TLR happened in winter and spring, with a shallower TLR in summer. Large seasonal variations of TLR could be seen in Northeast China, where there was a steep TLR in spring and summer and a strong surface temperature vertical gradient inversion in winter. The smallest seasonal variation of TLR happened in Central and Southwest China, especially in the Ta-pa Mountains and the Qinling Mountains. The TLR at very high altitudes (>5 km) was usually steeper than at low altitudes, in all months of the year.

scholarly journals Climatology of stratocumulus cloud morphologies: microphysical properties and radiative effects

2014 ◽  
Vol 14 (13) ◽  
pp. 6695-6716 ◽  
Author(s):  
A. Muhlbauer ◽  
I. L. McCoy ◽  
R. Wood
Keyword(s):  
Physical Properties ◽  
Global Scale ◽  
Cloud Fraction ◽  
Cloud Base ◽  
Trade Wind ◽  
Radiative Effects ◽  
Regional Variability ◽  
Microphysical Properties ◽  
Low Clouds ◽  
Low Cloud

Abstract. An artificial neural network cloud classification scheme is combined with A-train observations to characterize the physical properties and radiative effects of marine low clouds based on their morphology and type of mesoscale cellular convection (MCC) on a global scale. The cloud morphological categories are (i) organized closed MCC, (ii) organized open MCC and (iii) cellular but disorganized MCC. Global distributions of the frequency of occurrence of MCC types show clear regional signatures. Organized closed and open MCCs are most frequently found in subtropical regions and in midlatitude storm tracks of both hemispheres. Cellular but disorganized MCC are the predominant type of marine low clouds in regions with warmer sea surface temperature such as in the tropics and trade wind zones. All MCC types exhibit a pronounced seasonal cycle. The physical properties of MCCs such as cloud fraction, radar reflectivity, drizzle rates and cloud top heights as well as the radiative effects of MCCs are found highly variable and a function of the type of MCC. On a global scale, the cloud fraction is largest for closed MCC with mean cloud fractions of about 90%, whereas cloud fractions of open and cellular but disorganized MCC are only about 51% and 40%, respectively. Probability density functions (PDFs) of cloud fractions are heavily skewed and exhibit modest regional variability. PDFs of column maximum radar reflectivities and inferred cloud base drizzle rates indicate fundamental differences in the cloud and precipitation characteristics of different MCC types. Similarly, the radiative effects of MCCs differ substantially from each other in terms of shortwave reflectance and transmissivity. These differences highlight the importance of low-cloud morphologies and their associated cloudiness on the shortwave cloud forcing.

scholarly journals Occurrence and growth of sub-50 nm aerosol particles in the Amazonian boundary layer

2021 ◽  
Author(s):  
Marco A. Franco ◽  
Florian Ditas ◽  
Leslie Ann Kremper ◽  
Luiz A. T. Machado ◽  
Meinrat O. Andreae ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Meteorological Data ◽  
Cloud Condensation Nuclei ◽  
Deep Convection ◽  
Potential Temperature ◽  
Meteorological Conditions ◽  
Subsequent Growth ◽  
Wet Season ◽  
Particle Injection ◽  
Continuous Growth

Abstract. New particle formation (NPF), referring to the nucleation of molecular clusters and their subsequent growth into the cloud condensation nuclei (CCN) size range, is a globally significant and climate-relevant source of atmospheric aerosols. Classical NPF exhibiting continuous growth from a few nanometers to the Aitken mode around 60–70 nm is widely observed in the planetary boundary layer (PBL) around the world, but not in central Amazonia. Here, classical NPF events are rarely observed in the PBL, but instead, NPF begins in the upper troposphere (UT), followed by downdraft injection of sub-50 nm (CN< 50) particles into the PBL and their subsequent growth. Central aspects of our understanding of these processes in the Amazon have remained enigmatic, however. Based on more than six years of aerosol and meteorological data from the Amazon Tall Tower Observatory (ATTO, Feb 2014 to Sep 2020), we analyzed the diurnal and seasonal patterns as well as meteorological conditions during 254 of such Amazonian growth events on 217 event days, which show a sudden occurrence of particles between 10 and 50 nm in the PBL, followed by their growth to CCN sizes. The occurrence of events was significantly higher during the wet season, with 88 % of all events from January to June, than during the dry season, with 12 % from July to December, probably due to differences in the condensation sink (CS), atmospheric aerosol load, and meteorological conditions. Across all events, a median growth rate (GR) of 5.2 nm h−1 and a median CS of 0.0011 s−1 were observed. The growth events were more frequent during the daytime (74 %) and showed higher GR (5.9 nm h−1) compared to nighttime events (4.0 nm h−1), emphasizing the role of photochemistry and PBL evolution in particle growth. About 70 % of the events showed a negative anomaly of the equivalent potential temperature (∆θ'e) – as a marker for downdrafts – and a low satellite brightness temperature (Tir) – as a marker for deep convective clouds – in good agreement with particle injection from the UT in the course of strong convective activity. About 30 % of the events, however, occurred in the absence of deep convection, partly under clear sky conditions, and with a positive ∆θ'e anomaly. Therefore, these events do not appear to be related to downdraft injection and suggest the existence of other currently unknown sources of the sub-50 nm particles.

scholarly journals Characterisation and surface radiative impact of Arctic low clouds from the IAOOS field experiment

2020 ◽  
Author(s):  
Julia Maillard ◽  
François Ravetta ◽  
Jean-Christophe Raut ◽  
Vincent Mariage ◽  
Jacques Pelon
Keyword(s):  
Field Experiment ◽  
Arctic Ocean ◽  
Cloud Cover ◽  
Great Majority ◽  
The North ◽  
Low Clouds ◽  
Radiative Impact ◽  
Low Cloud ◽  
Ocean Observing ◽  
Layer Cloud

Abstract. The Ice, Atmosphere, Arctic Ocean Observing System (IAOOS) field experiment took place from 2014 to 2019. Over this period, more than 20 instrumented buoys were deployed at the North Pole. Once locked into the ice, the buoys drifted for periods of a month to more than a year. Some of these buoys were equipped with 808 nm wavelength lidars which acquired a total of 1805 profiles over the course of the campaign. This IAOOS lidar dataset is exploited to establish a novel statistic of cloud cover and of the geometrical and optical characteristics of the lowest cloud layer. Cloud frequency is globally at 75 %, and above 85 % from May to October. Single layers are thickest in October/November and thinnest in the summer. Meanwhile, their optical depth is maximum in October. On the whole, the cloud cover is very low, with the great majority of first layer bases beneath 120 m. In the shoulder seasons, surface temperatures are markedly warmer when the IAOOS profile contains at least one low cloud than when it does not. This temperature difference is statistically insignificant in the summer months. Indeed, summer clouds have a shortwave cooling effect which can reach −60 W m−2 and balance out their longwave warming effect.

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