Changes in HIRS Detection of Cloud over Australia from 1985 to 2001

A long-term archive of cloud properties (cloud top pressure, CTP; and cloud effective emissivity, ε) determined from High-resolution Infrared Radiation Sounder (HIRS) data is investigated for evidence of regional cloud cover change. In the 17 years between 1985 and 2001, different cloud types are analysed over the Australian region (10° S–45° S, 105° E–160° E) and areas of change in total cloud frequency examined. Total cloud frequency change over the Australian region between two adjacent eight-year time periods (1994 to 2001 minus 1985 to 1992) shows the largest increases (ranges between 6% and 12%) of average HIRS total cloud cover occurring over the offshore regions to the northwest and northeast of the continent. Over land, the largest reduction of average HIRS total cloud frequency is in the southwestern region of Australia (between 2% and 8%). Through central Australia, there is a 2% to 7% increase in average HIRS total cloud frequency when comparing these eight-year periods. This paper examines the regional cloud changes in 17 years over Australia that are embedded in global cloud statistics. Examining total HIRS cloud cover frequency over Australia and comparing two different eight-year time periods, has highlighted notable areas of average change. Preliminary reporting of satellite-derived HIRS cloud products and Global Precipitation Climatology Project (GPCP) rainfall products during La Niña seasons between 1985 and 2001 has also been undertaken.

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Observed trends in clouds and precipitation (1983–2009): implications for their cause(s)

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-4899-2021 ◽

2021 ◽

Vol 21 (6) ◽

pp. 4899-4913

Author(s):

Xiang Zhong ◽

Shaw Chen Liu ◽

Run Liu ◽

Xinlu Wang ◽

Jiajia Mo ◽

...

Keyword(s):

Global Warming ◽

Cloud Cover ◽

Global Analysis ◽

Hadley Circulation ◽

Underlying Mechanism ◽

Global Precipitation Climatology Project ◽

Global Temperature ◽

Total Cloud Cover ◽

Global Precipitation ◽

Linear Trends

Abstract. Satellite observations (International Satellite Cloud Climatology Project (ISCCP), 1983–2009) of linear trends in cloud cover are compared to those in global precipitation (Global Precipitation Climatology Project (GPCP) pentad V2.2, 1983–2009), to investigate possible cause(s) of the linear trends in both cloud cover and precipitation. The spatial distributions of the linear trends in total cloud cover and precipitation are both characterized primarily by a broadening of the major ascending zone of Hadley circulation. Our correlation studies suggest that global warming, Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) can explain 67 %, 49 % and 38 %, respectively, of the spatial variabilities in the linear trends in cloud cover, but causality is harder to establish. Further analysis of the broadening of the major ascending zone of Hadley circulation shows that the trend in global temperature, rather than those in AMO and PDO, is the primary contributor to the observed linear trends in total cloud cover and precipitation in 1983–2009. The underlying mechanism driving this broadening is proposed to be the moisture–convection–latent-heat feedback cycle under global warming conditions. The global analysis is extended by investigating connections between clouds and precipitation in China, based on a large number of long-running, high-quality surface weather stations in 1957–2005. This reveals a quantitative matching relationship between the reduction in light precipitation and the reduction in total cloud cover. Furthermore, our study suggests that the reduction in cloud cover in China is primarily driven by global temperature; PDO plays a secondary role, while the contribution from AMO and Niño3.4 is insignificant, consistent with the global analysis.

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Diagnosis of Variability and Trends in a Global Precipitation Dataset Using a Physically Motivated Statistical Model

Journal of Climate ◽

10.1175/jcli3849.1 ◽

2006 ◽

Vol 19 (17) ◽

pp. 4154-4166 ◽

Cited By ~ 8

Author(s):

M. R. P. Sapiano ◽

D. B. Stephenson ◽

H. J. Grubb ◽

P. A. Arkin

Keyword(s):

Temperature Gradient ◽

Statistical Model ◽

Linear Models ◽

Precipitation Amount ◽

Global Precipitation Climatology Project ◽

Specific Humidity ◽

Distribution Model ◽

Meridional Temperature Gradient ◽

Global Precipitation

Abstract A physically motivated statistical model is used to diagnose variability and trends in wintertime (October–March) Global Precipitation Climatology Project (GPCP) pentad (5-day mean) precipitation. Quasigeostrophic theory suggests that extratropical precipitation amounts should depend multiplicatively on the pressure gradient, saturation specific humidity, and the meridional temperature gradient. This physical insight has been used to guide the development of a suitable statistical model for precipitation using a mixture of generalized linear models: a logistic model for the binary occurrence of precipitation and a Gamma distribution model for the wet day precipitation amount. The statistical model allows for the investigation of the role of each factor in determining variations and long-term trends. Saturation specific humidity qs has a generally negative effect on global precipitation occurrence and with the tropical wet pentad precipitation amount, but has a positive relationship with the pentad precipitation amount at mid- and high latitudes. The North Atlantic Oscillation, a proxy for the meridional temperature gradient, is also found to have a statistically significant positive effect on precipitation over much of the Atlantic region. Residual time trends in wet pentad precipitation are extremely sensitive to the choice of the wet pentad threshold because of increasing trends in low-amplitude precipitation pentads; too low a choice of threshold can lead to a spurious decreasing trend in wet pentad precipitation amounts. However, for not too small thresholds, it is found that the meridional temperature gradient is an important factor for explaining part of the long-term trend in Atlantic precipitation.

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Long Term Monitoring and Connection between Topography and Cloud Cover Distribution in Serbia

Atmosphere ◽

10.3390/atmos12080964 ◽

2021 ◽

Vol 12 (8) ◽

pp. 964

Author(s):

Aleksandar Valjarević ◽

Cezar Morar ◽

Jelena Živković ◽

Liudmyla Niemets ◽

Dušan Kićović ◽

...

Keyword(s):

Cloud Cover ◽

Winter Season ◽

Kendall Test ◽

High Elevation ◽

Cloud Properties ◽

Moderate Resolution ◽

Long Term Monitoring ◽

Moderate Resolution Imaging Spectroradiometer ◽

Term Monitoring

The use of weather satellite recordings has been growing rapidly over the last three decades. Determining the patterns between meteorological and topographical features is an important scientific job. Cloud cover analysis and properties can be of the utmost significance for potential cloud seeding. Here, the analysis of the cloud properties was conducted by means of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite recordings. The resolution of used data was 1 km2 within the period of 30 years (1989–2019). This research showed moderate changing of cloudiness in the territory of Serbia with a high cloudiness in February, followed by cloudiness in January and November. For the past three decades, May has been the month with the highest cloudiness. The regions in the east and south-west, and particularly in the west, have a high absolute cloudiness, which is connected with the high elevation of the country. By means of long term monitoring, the whole territory of Serbia was analyzed for the first time, in terms of cloudiness. Apart from the statistical and numerical results obtained, this research showed a connection between relief and clouds, especially in the winter season. Linear regression MK (Mann–Kendall test) has proven this theory right, connecting high elevation sides with high absolute cloudiness through the year.

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A study of the impact of synoptic weather conditions and water vapor on aerosol-cloud relationships over major urban clusters of China

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-14007-2015 ◽

2015 ◽

Vol 15 (10) ◽

pp. 14007-14026

Author(s):

K. Kourtidis ◽

S. Stathopoulos ◽

A. K. Georgoulias ◽

G. Alexandri ◽

S. Rapsomanikis

Keyword(s):

Water Vapor ◽

Cloud Cover ◽

Hydrological Cycle ◽

Weather Conditions ◽

Climatic Impact ◽

Sea Level Pressure ◽

Synoptic Weather ◽

Cloud Top Pressure ◽

The Impact

Abstract. The relationships between Aerosol Optical Depth (AOD) and Cloud Cover (CC) over 3 major urban clusters in China are studied under different Sea Level Pressure (SLP) and Water Vapor (WV) regimes using a decade (2003–2013) of MODIS observations. Over all urban clusters, for all SLP regimes, CC is found to increase with AOD, thus pointing out that the CC dependence on AOD is not solely due to meteorological co-variability. WV is found to have a stronger impact on CC than AOD. This impact is more pronounced at high aerosol load than at low aerosol load. Hence, studies of AOD-CC relationships based on satellite data, might greatly overestimate or underestimate the AOD impact on CC in regions where AOD and WV have similar or opposite seasonal variations, respectively. Further, this impact shows that the hydrological cycle interferes with the aerosol climatic impact and we need to improve our understanding of this interference. Our results also suggest that studies attributing Cloud Top Pressure (CTP) long-term changes to changes in aerosol load might have a WV bias.

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Evaluating the Consistency and Continuity of Pixel-Scale Cloud Property Data Records From Aqua and SNPP

10.5194/amt-2021-391 ◽

2021 ◽

Author(s):

Qing Yue ◽

Eric J. Fetzer ◽

Likun Wang ◽

Brian H. Kahn ◽

Nadia Smith ◽

...

Keyword(s):

Global Environmental Change ◽

Retrieval Algorithm ◽

Cloud Data ◽

Cloud Properties ◽

Global Environmental ◽

Property Data ◽

Atmospheric Profile ◽

Cloud Top Pressure ◽

Pixel Scale

Abstract. The Aqua, SNPP, and JPSS satellites carry a combination of hyperspectral infrared sounders (AIRS, CrIS) and high-spatial-resolution narrowband imagers (MODIS, VIIRS). They provide an opportunity to acquire high-quality long-term cloud data records and are a key component of the existing Program of Record of cloud observations. By matching observations from sounders and imagers across different platforms at pixel scale, this study evaluates the self-consistency and continuity of cloud retrievals from Aqua and SNPP by multiple algorithms, including the AIRS Version-7 retrieval algorithm and the Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) Version-2 for sounders, and the Standard Aqua-MODIS Collection-6.1 and the NASA MODIS-VIIRS continuity cloud products for imagers. Metrics describing detailed statistical distributions at sounder field of view (FOV) and the joint histograms of cloud properties are evaluated. These products are found highly consistent despite their retrieval from different sensors using different algorithms. Differences between the two sounder cloud products are mainly due to cloud clearing and treatment of clouds in scenes with unsuccessful atmospheric profile retrievals. The sounder subpixel cloud heterogeneity evaluated using the standard deviation of imager retrievals at sounder FOV shows good agreement between the standard and continuity products from different satellites. However, impact of algorithm and instrument differences between MODIS and VIIRS is revealed in cloud top pressure retrievals and in the imager cloud distribution skewness. Our study presents a unique aspect to examine NASA’s progress toward building a continuous cloud data record with sufficient quality to investigate clouds’ role in global environmental change.

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PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

Scientific Data ◽

10.1038/s41597-021-00940-9 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Mojtaba Sadeghi ◽

Phu Nguyen ◽

Matin Rahnamay Naeini ◽

Kuolin Hsu ◽

Dan Braithwaite ◽

...

Keyword(s):

Spatial Resolution ◽

Heavy Precipitation ◽

Climate Data ◽

Spatiotemporal Resolution ◽

Data Record ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Global Precipitation ◽

Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

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Cloud Cover and Aurora Contamination at Dome A in 2017 from KLCAM

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa3824 ◽

2020 ◽

Author(s):

Xu Yang ◽

Zhaohui Shang ◽

Keliang Hu ◽

Yi Hu ◽

Bin Ma ◽

...

Keyword(s):

Cloud Cover ◽

Emission Lines ◽

Austral Winter ◽

Site Testing ◽

Dome A ◽

Astronomical Observations ◽

Auroral Emission ◽

Long Term Monitoring ◽

Term Monitoring

Abstract Dome A in Antarctica has many characteristics that make it an excellent site for astronomical observations, from the optical to the terahertz. Quantitative site testing is still needed to confirm the site’s properties. In this paper, we present a statistical analysis of cloud cover and aurora contamination from the Kunlun Cloud and Aurora Monitor (KLCAM). KLCAM is an automatic, unattended all-sky camera aiming for long-term monitoring of the usable observing time and optical sky background at Dome A. It was installed at Dome A in January 2017, worked through the austral winter, and collected over 47,000 images over 490 days. A semi-quantitative visual data analysis of cloud cover and auroral contamination was carried out by five individuals. The analysis shows that the night sky was free of clouds for 83 per cent of the time, which ranks Dome A highly in a comparison with other observatory sites. Although aurorae were detected somewhere on an image for nearly 45 per cent of the time, the chance of a point on the sky being affected by an aurora is small. The strongest auroral emission lines can be filtered out with customized filters.

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The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

Remote Sensing ◽

10.3390/rs13091701 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1701

Author(s):

Leonardo Bagaglini ◽

Paolo Sanò ◽

Daniele Casella ◽

Elsa Cattani ◽

Giulia Panegrossi

Keyword(s):

Neural Network ◽

Passive Microwave ◽

Retrieval Algorithm ◽

Climate Data ◽

High Quality ◽

Data Record ◽

Global Precipitation ◽

Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

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Seasonal cycle of cloud cover analyzed using Meteosat images

Annales Geophysicae ◽

10.1007/s00585-998-0331-3 ◽

1998 ◽

Vol 16 (3) ◽

pp. 331-341 ◽

Cited By ~ 5

Author(s):

J. Massons ◽

D. Domingo ◽

J. Lorente

Keyword(s):

Spatial Resolution ◽

Seasonal Cycle ◽

Cloud Cover ◽

High Spatial Resolution ◽

Detection Method ◽

Cloud Amount ◽

Cloud Detection ◽

Total Cloud Amount ◽

Cloud Parameters ◽

Cloud Properties

Abstract. A cloud-detection method was used to retrieve cloudy pixels from Meteosat images. High spatial resolution (one pixel), monthly averaged cloud-cover distribution was obtained for a 1-year period. The seasonal cycle of cloud amount was analyzed. Cloud parameters obtained include the total cloud amount and the percentage of occurrence of clouds at three altitudes. Hourly variations of cloud cover are also analyzed. Cloud properties determined are coherent with those obtained in previous studies.Key words. Cloud cover · Meteosat

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The use of artificial neural networks in the problem of classifying cloud types in wide-angle images of the visible hemisphere of the sky.

10.5194/egusphere-egu21-14470 ◽

2021 ◽

Author(s):

Nikita Veremev

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Cloud Cover ◽

Human Factor ◽

Wide Angle ◽

Reliable Determination ◽

Air Masses ◽

Key Aspects

Within the framework of meteorology and oceanology, the importance of the cloud mass and the type of clouds cannot be underestimated. When describing and studying weather, precipitation and the movement of air masses over the ocean, the amount and type of clouds determines the flows of precipitation, their intensity, helps to predict the weather and the content of various impurities in the air, which makes the study of the properties of cloud cover one of the key aspects of meteorological and oceanological research.The types of clouds are determined by the specialist, visually comparing the picture of the sky over the ocean with the guideline documents, the use of which reduces the possibility of the human factor affecting the determination of these parameters.For an accurate study, study of the dynamics and dependence of climatic models on the conditions of cloud types, long-term measurements of the same type and the continuity of their methods are required. However, all these data are very unevenly distributed over the Earth's surface, and the number of ship observations is greatly reduced.Thus, taking into account the importance of reliable determination of data related to cloudiness and the problems of their accuracy, the relevance and need to automate the determination of cloud types are obvious.As a result of the work, an algorithm was obtained that allows classifying cloud types based on photographs taken during long-term sea expeditions.

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