scholarly journals Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

2008 ◽  
Vol 23 (5) ◽  
pp. 914-930 ◽  
Author(s):  
Ryan L. Fogt ◽  
David H. Bromwich
Keyword(s):  
Liquid Water ◽  
Cloud Cover ◽  
Supercooled Liquid ◽  
South Pole ◽  
Atmospheric Moisture ◽  
Microphysics Scheme ◽  
Low Level ◽  
High Level ◽  
Cloud Ice ◽  
Moisture Variability

Abstract Antarctic Mesoscale Prediction System (AMPS) forecasts of atmospheric moisture and cloud fraction (CF) are compared with observations at McMurdo and Amundsen–Scott South Pole station (hereafter, South Pole station) in Antarctica. Overall, it is found that the model produces excessive moisture at both sites in the mid- to upper troposphere because of a weaker vertical decrease of moisture in AMPS than observed. Correlations with observations suggest AMPS does a reasonable job of capturing the low-level moisture variability at McMurdo and the upper-level moisture variability at South Pole station. The model underpredicts the cloud cover at both locations, but changes to the AMPS empirical CF algorithm remove this negative bias by more than doubling the weight given to the cloud ice path. A “pseudosatellite” product based on the microphysical quantities of cloud ice and cloud liquid water within AMPS is preliminarily evaluated against Defense Meteorological Satellite Program (DMSP) imagery during summer to examine the broader performance of cloud variability in AMPS. These comparisons reveal that the model predicts high-level cloud cover and movement with fidelity, which explains the good agreement between the modified CF algorithm and the observed CF. However, this product also demonstrates deficiencies in capturing low-level cloudiness over cold ice surfaces primarily related to insufficient supercooled liquid water produced by the microphysics scheme, which also reduces the CF correlation with observations. The results suggest that AMPS predicts the overall CF amount and high cloud variability notably well, making it a reliable tool for longer-term climate studies of these fields in Antarctica.

scholarly journals The ability of the ICE-T microphysics scheme in HARMONIE-AROME to predict aircraft icing

2021 ◽  
Author(s):  
Tim Carlsen ◽  
Morten Køltzow ◽  
Trude Storelvmo
Keyword(s):  
Water Content ◽  
Numerical Weather Prediction ◽  
Liquid Water ◽  
Prediction Models ◽  
Weather Prediction ◽  
Supercooled Liquid ◽  
Liquid Water Content ◽  
Aircraft Icing ◽  
Microphysics Scheme ◽  
Numerical Weather

Abstract In-cloud icing is a major hazard for aviation traffic and forecasting of these events is an important task for weather agencies worldwide. A common tool utilised by aviation forecasters is an icing intensity index based on supercooled liquid water from numerical weather prediction models. We seek to validate the modified microphysics scheme, ICE-T, in the HARMONIE-AROME numerical weather prediction model with respect to aircraft icing. Icing intensities and supercooled liquid water derived from two 3-month winter season simulations with the original microphysics code, CTRL, and ICE-T are compared with pilot reports of icing and satellite retrieved values of liquid and ice water content from CloudSat-CALIPSO and liquid water path from AMSR-2. The results show increased supercooled liquid water and higher icing indices in ICE-T. Several different thresholds and sizes of neighbourhood areas for icing forecasts were tested out, and ICE-T captures more of the reported icing events for all thresholds and nearly all neighbourhood areas. With a higher frequency of forecasted icing, a higher false-alarm ratio cannot be ruled out, but is not possible to quantify due to the lack of no-icing observations. The increased liquid water content in ICE-T shows a better match with the retrieved satellite observations, yet the values are still greatly underestimated at lower levels. Future studies should investigate this issue further, as liquid water content also has implications for downstream processes such as the cloud radiative effect, latent heat release, and precipitation.

scholarly journals On the decadal increase in the tropical mean outgoing longwave radiation for the period 1984-2000

2004 ◽  
Vol 4 (5) ◽  
pp. 1419-1425 ◽  
Author(s):  
D. Hatzidimitriou ◽  
I. Vardavas ◽  
K. G. Pavlakis ◽  
N. Hatzianastassiou ◽  
C. Matsoukas ◽  
...  
Keyword(s):  
Time Series ◽  
Outgoing Longwave Radiation ◽  
Cloud Cover ◽  
Longwave Radiation ◽  
Atmospheric Temperature ◽  
Tropical Region ◽  
Transfer Model ◽  
Cloud Data ◽  
Low Level ◽  
High Level

Abstract. In the present paper, we have calculated the outgoing longwave radiation at the top of the atmosphere (OLR at TOA) using a deterministic radiation transfer model, cloud data from ISCCP-D, and atmospheric temperature and humidity data from NCEP/NCAR reanalysis, for the seventeen-year period 1984-2000. We constructed anomaly time-series of the OLR at TOA, as well as of all of the key input climatological data, averaged in the tropical region between 20°N and 20°S. We compared the anomaly time-series of the model calculated OLR at TOA with that obtained from the ERBE S-10N (WFOV NF edition 2) non-scanner measurements. The model results display very similar seasonal and inter-annual variability as the ERBS data, and indicate a decadal increase of OLR at TOA of 1.9±0.2Wm-2/decade, which is lower than that displayed by the ERBS time-series (3.5±0.3Wm-2). Analysis of the inter-annual and long-term variability of the various parameters determining the OLR at TOA, showed that the most important contribution to the observed trend comes from a decrease in high-level cloud cover over the period 1984-2000, followed by an apparent drying of the upper troposphere and a decrease in low-level cloudiness. Opposite but small trends are introduced by a decrease in low-level cloud top pressure, an apparent cooling of the lower stratosphere (at the 50mbar level) and a small decadal increase in mid-level cloud cover.

scholarly journals Water vapor in cold and clean atmosphere: a 3-year data set in the boundary layer of Dome C, East Antarctic Plateau

2022 ◽  
Author(s):  
Christophe Genthon ◽  
Dana E. Veron ◽  
Etienne Vignon ◽  
Jean-Baptiste Madeleine ◽  
Luc Piard
Keyword(s):  
Liquid Water ◽  
Supercooled Liquid ◽  
Data Repository ◽  
Atmospheric Moisture ◽  
Equilibrium Conditions ◽  
Data Set ◽  
Antarctic Plateau ◽  
The Antarctic ◽  
Natural Laboratory ◽  
Clean Atmosphere

Abstract. The air at the surface of the high Antarctic Plateau is very cold, dry and clean. In such conditions the atmospheric moisture can significantly deviate from thermodynamic equilibrium conditions, and supersaturation with respect to ice can occur. Most conventional humidity sensors for meteorological applications cannot report supersaturation in this environment. A simple approach for measuring supersaturation using conventional instruments, one being operated in a heated airflow, is presented. Since 2018, this instrumental setup was deployed at 3 levels in the lower ~40 m above the surface at Dome C on the high Antarctic Plateau. The 3-year 2018–2020 record (Genthon et al. 2021) is presented and analyzed for features such as the frequency of supersaturation with respect to ice, diurnal and seasonal variability, and vertical distribution. As supercooled liquid water droplets are frequently observed in clouds at the temperatures met on the high Antarctic Plateau, the distribution of relative humidity with respect to liquid water at Dome C is also discussed. It is suggested that, while not strictly mimicking the conditions of the high troposphere, the surface atmosphere on the Antarctic Plateau is a convenient natural laboratory to test parametrizations of cold microphysics predominantly developed to handle the genesis of high tropospheric clouds. Data are distributed on the PANGAEA data repository at https://doi.pangaea.de/10.1594/PANGAEA.939425 (Genthon et al., 2021).

scholarly journals SegCloud: a novel cloud image segmentation model using deep Convolutional Neural Network for ground-based all-sky-view camera observation

2019 ◽  
Author(s):  
Wanyi Xie ◽  
Dong Liu ◽  
Ming Yang ◽  
Shaoqing Chen ◽  
Benge Wang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Cloud Cover ◽  
Weather Forecast ◽  
Cloud Detection ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Softmax Classifier ◽  
Discrimination Ability ◽  
Low Level ◽  
High Level

Abstract. Cloud detection and cloud properties have significant applications in weather forecast, signal attenuation analysis, and other cloud-related fields. Cloud image segmentation is the fundamental and important step to derive cloud cover. However, traditional segmentation methods rely on low-level visual features of clouds, and often fail to achieve satisfactory performance. Deep Convolutional Neural Networks (CNNs) are able to extract high-level feature information of object and have become the dominant methods in many image segmentation fields. Inspired by that, a novel deep CNN model named SegCloud is proposed and applied to accurate cloud segmentation based on ground-based observation. Architecturally, SegCloud possesses symmetric encoder-decoder structure. The encoder network combines low-level cloud features to form high-level cloud feature maps with low resolution, and the decoder network restores the obtained high-level cloud feature maps to the same resolution of input images. The softmax classifier finally achieves pixel-wise classification and outputs segmentation results. SegCloud has powerful cloud discrimination ability and can automatically segment the whole sky images obtained by a ground-based all-sky-view camera. Furthermore, a new database, which includes 400 whole sky images and manual-marked labels, is built to train and test the SegCloud model. The performance of SegCloud is validated by extensive experiments, which show that SegCloud is effective and accurate for ground-based cloud segmentation and achieves better results than traditional methods. Moreover, the accuracy and practicability of SegCloud is further proved by applying it to cloud cover estimation.

scholarly journals Simulation of Late Summer Arctic Clouds during ASCOS with Polar WRF

2017 ◽  
Vol 145 (2) ◽  
pp. 521-541 ◽  
Author(s):  
Keith M. Hines ◽  
David H. Bromwich
Keyword(s):  
Liquid Water ◽  
Climate Models ◽  
Longwave Radiation ◽  
Late Summer ◽  
Shortwave Radiation ◽  
The Arctic ◽  
Microphysics Scheme ◽  
Cloud Liquid Water ◽  
Low Level ◽  
Outer Domain

Low-level clouds are extensive in the Arctic and contribute to inadequately understood feedbacks within the changing regional climate. The simulation of low-level clouds, including mixed-phase clouds, over the Arctic Ocean during summer and autumn remains a challenge for both real-time weather forecasts and climate models. Here, improved cloud representations are sought with high-resolution mesoscale simulations of the August–September 2008 Arctic Summer Cloud Ocean Study (ASCOS) with the latest polar-optimized version (3.7.1) of the Weather Research and Forecasting (Polar WRF) Model with the advanced two-moment Morrison microphysics scheme. Simulations across several synoptic regimes for 10 August–3 September 2008 are performed with three domains including an outer domain at 27-km grid spacing and nested domains at 9- and 3-km spacing. These are realistic horizontal grid spacings for common mesoscale applications. The control simulation produces excessive cloud liquid water in low clouds resulting in a large deficit in modeled incident shortwave radiation at the surface. Incident longwave radiation is less sensitive. A change in the sea ice albedo toward the larger observed values during ASCOS resulted in somewhat more realistic simulations. More importantly, sensitivity tests show that a reduction in specified liquid cloud droplet number to very pristine conditions increases liquid precipitation, greatly reduces the excess in simulated low-level cloud liquid water, and improves the simulated incident shortwave and longwave radiation at the surface.

scholarly journals On the decadal increase in the tropical mean outgoing longwave radiation for the period 1984–2000

2004 ◽  
Vol 4 (3) ◽  
pp. 2727-2745
Author(s):  
D. Hatzidimitriou ◽  
I. Vardavas ◽  
K. G. Pavlakis ◽  
N. Hatzianastassiou ◽  
C. Matsoukas ◽  
...  
Keyword(s):  
Time Series ◽  
Outgoing Longwave Radiation ◽  
Cloud Cover ◽  
Longwave Radiation ◽  
Atmospheric Temperature ◽  
Tropical Region ◽  
Transfer Model ◽  
Cloud Data ◽  
Low Level ◽  
High Level

Abstract. In the present paper, we have calculated the outgoing longwave radiation at the top of the atmosphere (OLR at TOA) using a deterministic radiation transfer model, cloud data from ISCCP-D, and atmospheric temperature and humidity data from NCEP/NCAR reanalysis, for the seventeen-year period 1984–2000. We constructed anomaly time-series of the OLR at TOA, as well as of all of the key input climatological data, averaged in the tropical region between 20° N and 20° S. We compared the anomaly time-series of the model calculated OLR at TOA with that obtained from the ERBE S-10N (WFOV NF edition 2) non-scanner measurements. The model results display very similar seasonal and inter-annual variability as the ERBS data, and indicate a decadal increase of OLR at TOA of 1.9±0.2 Wm−2/decade, which is lower than that displayed by the ERBS time-series (3.5±0.3 Wm−2). Analysis of the inter-annual and long-term variability of the various parameters determining the OLR at TOA, showed that the most important contribution to the observed trend comes from a decrease in high-level cloud cover over the period 1984–2000, followed by an apparent drying of the upper troposphere and a decrease in low-level cloudiness. Opposite but small trends are introduced by a decrease in low-level cloud top pressure, an apparent cooling of the lower stratosphere (at the 50 mbar level) and a small decadal increase in mid-level cloud cover.

scholarly journals Antarctic clouds, supercooled liquid water and mixed phase, investigated with DARDAR: geographical and seasonal variations

2019 ◽  
Vol 19 (10) ◽  
pp. 6771-6808 ◽  
Author(s):  
Constantino Listowski ◽  
Julien Delanoë ◽  
Amélie Kirchgaessner ◽  
Tom Lachlan-Cope ◽  
John King
Keyword(s):  
Sea Ice ◽  
Liquid Water ◽  
Open Water ◽  
Supercooled Liquid ◽  
Cloud Fraction ◽  
Mixed Phase ◽  
Low Level ◽  
Complementary Part ◽  
Mixed Phase Clouds ◽  
Ice Fraction

Abstract. Antarctic tropospheric clouds are investigated using the DARDAR (raDAR/liDAR)-MASK products between 60 and 82∘ S. The cloud fraction (occurrence frequency) is divided into the supercooled liquid-water-containing cloud (SLC) fraction and its complementary part called the all-ice cloud fraction. A further distinction is made between SLC involving ice (mixed-phase clouds, MPC) or not (USLC, for unglaciated SLC). The low-level (<3 km above surface level) SLC fraction is larger over seas (20 %–60 %), where it varies according to sea ice fraction, than over continental regions (0 %–35 %). The total SLC fraction is much larger over West Antarctica (10 %–40 %) than it is over the Antarctic Plateau (0 %–10 %). In East Antarctica the total SLC fraction – in summer for instance – decreases sharply polewards with increasing surface height (decreasing temperatures) from 40 % at the coast to <5% at 82∘ S on the plateau. The geographical distribution of the continental total all-ice fraction is shaped by the interaction of the main low-pressure systems surrounding the continent and the orography, with little association with the sea ice fraction. Opportunistic comparisons with published ground-based supercooled liquid-water observations at the South Pole in 2009 are made with our SLC fractions at 82∘ S in terms of seasonal variability, showing good agreement. We demonstrate that the largest impact of sea ice on the low-level SLC fraction (and mostly through the MPC) occurs in autumn and winter (22 % and 18 % absolute decrease in the fraction between open water and sea ice-covered regions, respectively), while it is almost null in summer and intermediate in spring (11 %). Monthly variability of the MPC fraction over seas shows a maximum at the end of summer and a minimum in winter. Conversely, the USLC fraction has a maximum at the beginning of summer. However, monthly evolutions of MPC and USLC fractions do not differ on the continent. This suggests a seasonality in the glaciation process in marine liquid-bearing clouds. From the literature, we identify the pattern of the monthly evolution of the MPC fraction as being similar to that of the aerosols in coastal regions, which is related to marine biological activity. Marine bioaerosols are known to be efficient ice-nucleating particles (INPs). The emission of these INPs into the atmosphere from open waters would add to the temperature and sea ice fraction seasonalities as factors explaining the MPC fraction monthly evolution.

scholarly journals SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation

2020 ◽  
Vol 13 (4) ◽  
pp. 1953-1961
Author(s):  
Wanyi Xie ◽  
Dong Liu ◽  
Ming Yang ◽  
Shaoqing Chen ◽  
Benge Wang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Cloud Cover ◽  
Weather Forecast ◽  
Cloud Detection ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Softmax Classifier ◽  
Low Level ◽  
Discrimination Capability ◽  
High Level

Abstract. Cloud detection and cloud properties have substantial applications in weather forecast, signal attenuation analysis, and other cloud-related fields. Cloud image segmentation is the fundamental and important step in deriving cloud cover. However, traditional segmentation methods rely on low-level visual features of clouds and often fail to achieve satisfactory performance. Deep convolutional neural networks (CNNs) can extract high-level feature information of objects and have achieved remarkable success in many image segmentation fields. On this basis, a novel deep CNN model named SegCloud is proposed and applied for accurate cloud segmentation based on ground-based observation. Architecturally, SegCloud possesses a symmetric encoder–decoder structure. The encoder network combines low-level cloud features to form high-level, low-resolution cloud feature maps, whereas the decoder network restores the obtained high-level cloud feature maps to the same resolution of input images. The Softmax classifier finally achieves pixel-wise classification and outputs segmentation results. SegCloud has powerful cloud discrimination capability and can automatically segment whole-sky images obtained by a ground-based all-sky-view camera. The performance of SegCloud is validated by extensive experiments, which show that SegCloud is effective and accurate for ground-based cloud segmentation and achieves better results than traditional methods do. The accuracy and practicability of SegCloud are further proven by applying it to cloud cover estimation.

Antarctic Cloud Property Retrievals from Infrared Radiances

2020 ◽  
Author(s):  
Penny Rowe ◽  
Von Walden ◽  
Matthew Fergoda ◽  
Connor Krill ◽  
Jonathon Gero ◽  
...  
Keyword(s):  
Liquid Water ◽  
Field Experiments ◽  
Climate Models ◽  
Complex Refractive Index ◽  
Supercooled Liquid ◽  
Radiative Properties ◽  
Retrieval Algorithm ◽  
South Pole ◽  
Infrared Radiance ◽  
Cloud Properties

&lt;p&gt;Clouds exert a strong radiative impact on the surface and have complicated effects that are still not well understood, particularly in the Antarctic. The amount of supercooled liquid water in Antarctic clouds, for example, is still poorly constrained, due to the low number of observations on the continent. It is also not clear how the radiative properties of supercooled liquid in those clouds should be represented in climate models. In particular, the complex refractive index (CRI) of liquid water is known to depend on temperature, but this dependence is typically ignored in climate models.&lt;/p&gt;&lt;p&gt;Here, we present cloud properties retrieved from Antarctic downwelling infrared radiance measurements made by an Atmospheric Emitted Radiance Interferometer (AERI) and by the Polar AERI (PAERI), using the CLoud and Atmospheric Radiation Retrieval Algorithm (CLARRA). Preliminary retrievals were made of cloud height, optical depth, thermodynamic phase, and effective radius for field experiments at Amundsen-Scott South Pole Station (2001) and at McMurdo Station (2016).&lt;/p&gt;&lt;p&gt;At South Pole, we find that clouds are typically thin and near the surface, in keeping with prior work. For thin clouds, the mode of the effective radii of liquid droplets (~4 &amp;#956;m) and ice particles (~15 &amp;#956;m in summer, ~12 &amp;#956;m in winter) at South Pole are found to be smaller than typical Arctic values (~9 &amp;#956;m for liquid and 17 to 25 &amp;#956;m for ice). Although ice cloud was found to dominate year-round at South Pole, significant supercooled liquid water was present in the summer. Cloud properties retrieved at South Pole will be compared to retrievals from McMurdo.&lt;/p&gt;&lt;p&gt;We further find that ignoring the temperature dependence of the CRI of supercooled liquid cloud leads to negative biases in part of the atmospheric window region (700 &amp;#8211; 1000 cm&lt;sup&gt;-1&lt;/sup&gt;), indicating underestimation of the greenhouse effect. These biases are expected to be partially offset by positive biases below 600 cm&lt;sup&gt;-1&lt;/sup&gt;. Based on these considerations, we recommend using temperature-dependent CRI for infrared radiance simulations of supercooled liquid water cloud.&lt;/p&gt;

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