scholarly journals Probability Distribution for the Visually Observed Fractional Cloud Cover over the Ocean

2018 ◽  
Vol 31 (8) ◽  
pp. 3207-3232 ◽  
Author(s):  
Marina Aleksandrova ◽  
Sergey K. Gulev ◽  
Konstantin Belyaev
Keyword(s):  
Probability Distribution ◽  
Cloud Cover ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
World Ocean ◽  
Accurate Estimation ◽  
Fractional Cloud ◽  
Low Cloud ◽  
Different Shapes ◽  
Low Cloud Cover

Abstract The authors suggest a three-parameter bounded distribution from the family of mixed gamma distributions for characterizing the probability density distributions of fractional total and low cloud cover over the global oceans. The authors derive both the continuous form of this distribution and its discrete counterpart, which can be directly applied to cloud cover reports. The distribution is applied to the cloud cover characteristics reported by voluntary observing ships (VOS) for the period from 1950 to 2011 after filtering nighttime observations with poor lunar illumination. The suggested distribution demonstrates a high goodness of fit to the data and good skill in capturing probability distributions with different shapes. The authors present seasonal climatologies of the parameters of the derived distribution for the chosen 60-yr period and demonstrate that applying the PDF-based concept to the analysis of cloud cover allows identification of areas where similar mean cloud amounts can be produced by probability distributions with very different shapes. The roles of the different parameters of the distribution in producing the observed cloud conditions in different regions of the World Ocean are discussed. The application of the derived probability distribution allows for accurate estimation of the percentiles of the distribution, which represent the probabilities of specific cloud conditions. These probabilities are presented for both total and low cloud cover, as well as for daytime and nighttime. The authors also discuss the applicability of the suggested distribution for the validation of different cloud cover data products over the globe and the prospects of additional applications.

scholarly journals Probability distributions for explaining hydrological losses in South Australian catchments

2013 ◽  
Vol 17 (11) ◽  
pp. 4541-4553 ◽  
Author(s):  
S. H. P. W. Gamage ◽  
G. A. Hewa ◽  
S. Beecham
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Distribution Functions ◽  
Accurate Estimation ◽  
Rainfall Runoff ◽  
Distribution Fitting ◽  
Distribution Method ◽  
Wide Variability ◽  
Anderson Darling

Abstract. Accurate estimation of hydrological losses is required for making vital decisions in design applications that are based on design rainfall models and rainfall–runoff models. The use of representative single values of hydrological losses, despite their wide variability, is common practice, especially in Australian studies. This practice leads to issues such as over or under estimation of design floods. The probability distribution method is potentially a better technique to describe losses. However, a lack of understanding of how losses are distributed can limit the use of this technique. This paper aims to identify a probability distribution function that can successfully describe hydrological losses of a catchment of interest. The paper explains the systematic process of identifying probability distribution functions, the problems faced during the distribution fitting process and a new generalised method to test the adequacy of fitted distributions. The goodness-of-fit of the fitted distributions are examined using the Anderson–Darling test and the Q–Q plot method and the errors associated with quantile estimation are quantified by estimating the bias and mean square error (MSE). A two-parameter gamma distribution was identified as one that successfully describes initial loss (IL) data for the selected catchments. Further, non-parametric standardised distributions that describe both IL and continuing loss data are also identified. This paper will provide a significant contribution to the Australian Rainfall and Runoff (ARR) guidelines that are currently being updated, by improving understanding of hydrological losses in South Australian catchments. More importantly, this study provides new knowledge on how IL in a catchment is characterised.

scholarly journals A best-fit probability distribution for the estimation of rainfall in northern regions of Pakistan

2016 ◽  
Vol 11 (1) ◽  
pp. 432-440 ◽  
Author(s):  
M. T. Amin ◽  
M. Rizwan ◽  
A. A. Alazba
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Future Research ◽  
Maximum Rainfall ◽  
Type Iii ◽  
Goodness Of Fit Tests ◽  
Pearson Type ◽  
Northern Regions ◽  
Best Fit

AbstractThis study was designed to find the best-fit probability distribution of annual maximum rainfall based on a twenty-four-hour sample in the northern regions of Pakistan using four probability distributions: normal, log-normal, log-Pearson type-III and Gumbel max. Based on the scores of goodness of fit tests, the normal distribution was found to be the best-fit probability distribution at the Mardan rainfall gauging station. The log-Pearson type-III distribution was found to be the best-fit probability distribution at the rest of the rainfall gauging stations. The maximum values of expected rainfall were calculated using the best-fit probability distributions and can be used by design engineers in future research.

scholarly journals Probability distributions for explaining hydrological losses in South Australian catchments

2013 ◽  
Vol 10 (4) ◽  
pp. 4597-4626
Author(s):  
S. H. P. W. Gamage ◽  
G. A. Hewa ◽  
S. Beecham
Keyword(s):  
Goodness Of Fit ◽  
Probability Distributions ◽  
Joint Probability ◽  
Accurate Estimation ◽  
Rainfall Runoff ◽  
Design Flood ◽  
Wide Variability ◽  
Multiple Variables ◽  
Two Parameter ◽  
Non Parametric

Abstract. The wide variability of hydrological losses in catchments is due to multiple variables that affect the rainfall-runoff process. Accurate estimation of hydrological losses is required for making vital decisions in design applications that are based on design rainfall models and rainfall-runoff models. Using representative single values of losses, despite their wide variability, is common practice, especially in Australian studies. This practice leads to issues such as over or under estimation of design floods. Probability distributions can be used as a better representation of losses. In particular, using joint probability approaches (JPA), probability distributions can be incorporated into hydrological loss parameters in design models. However, lack of understanding of loss distributions limits the benefit of using JPA. The aim of this paper is to identify a probability distribution function that can successfully describe hydrological losses in South Australian (SA) catchments. This paper describes suitable parametric and non-parametric distributions that can successfully describe observed loss data. The goodness-of-fit of the fitted distributions and quantification of the errors associated with quantile estimation are also discussed a two-parameter Gamma distribution was identified as one that successfully described initial loss (IL) data of the selected catchments. Also, a non-parametric standardised distribution of losses that describes both IL and continuing loss (CL) data were identified. The results obtained for the non-parametric methods were compared with similar studies carried out in other parts of Australia and a remarkable degree of consistency was observed. The results will be helpful in improving design flood applications.

scholarly journals Estimating the Best-Fitted Probability Distribution for Monthly Maximum Temperature at the Sylhet Station in Bangladesh

2021 ◽  
Vol 2 (2) ◽  
pp. 60-67
Author(s):  
Rashidul Hasan Rashidul Hasan
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Probability Model ◽  
Temperature Data ◽  
Maximum Temperature ◽  
Chi Square ◽  
Goodness Of Fit Tests ◽  
Anderson Darling ◽  
Log Normal

The estimation of a suitable probability model depends mainly on the features of available temperature data at a particular place. As a result, existing probability distributions must be evaluated to establish an appropriate probability model that can deliver precise temperature estimation. The study intended to estimate the best-fitted probability model for the monthly maximum temperature at the Sylhet station in Bangladesh from January 2002 to December 2012 using several statistical analyses. Ten continuous probability distributions such as Exponential, Gamma, Log-Gamma, Beta, Normal, Log-Normal, Erlang, Power Function, Rayleigh, and Weibull distributions were fitted for these tasks using the maximum likelihood technique. To determine the model’s fit to the temperature data, several goodness-of-fit tests were applied, including the Kolmogorov-Smirnov test, Anderson-Darling test, and Chi-square test. The Beta distribution is found to be the best-fitted probability distribution based on the largest overall score derived from three specified goodness-of-fit tests for the monthly maximum temperature data at the Sylhet station.

scholarly journals Synoptic-scale controls of fog and low-cloud variability in the Namib Desert

2020 ◽  
Vol 20 (6) ◽  
pp. 3415-3438 ◽  
Author(s):  
Hendrik Andersen ◽  
Jan Cermak ◽  
Julia Fuchs ◽  
Peter Knippertz ◽  
Marco Gaetani ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Cloud Cover ◽  
Large Scale ◽  
Marine Boundary Layer ◽  
Synoptic Scale ◽  
Namib Desert ◽  
Data Set ◽  
Air Masses ◽  
Low Cloud ◽  
Low Cloud Cover

Abstract. Fog is a defining characteristic of the climate of the Namib Desert, and its water and nutrient input are important for local ecosystems. In part due to sparse observation data, the local mechanisms that lead to fog occurrence in the Namib are not yet fully understood, and to date, potential synoptic-scale controls have not been investigated. In this study, a recently established 14-year data set of satellite observations of fog and low clouds in the central Namib is analyzed in conjunction with reanalysis data in order to identify synoptic-scale patterns associated with fog and low-cloud variability in the central Namib during two seasons with different spatial fog occurrence patterns. It is found that during both seasons, mean sea level pressure and geopotential height at 500 hPa differ markedly between fog/low-cloud and clear days, with patterns indicating the presence of synoptic-scale disturbances on fog and low-cloud days. These regularly occurring disturbances increase the probability of fog and low-cloud occurrence in the central Namib in two main ways: (1) an anomalously dry free troposphere in the coastal region of the Namib leads to stronger longwave cooling of the marine boundary layer, increasing low-cloud cover, especially over the ocean where the anomaly is strongest; (2) local wind systems are modulated, leading to an onshore anomaly of marine boundary-layer air masses. This is consistent with air mass back trajectories and a principal component analysis of spatial wind patterns that point to advected marine boundary-layer air masses on fog and low-cloud days, whereas subsiding continental air masses dominate on clear days. Large-scale free-tropospheric moisture transport into southern Africa seems to be a key factor modulating the onshore advection of marine boundary-layer air masses during April, May, and June, as the associated increase in greenhouse gas warming and thus surface heating are observed to contribute to a continental heat low anomaly. A statistical model is trained to discriminate between fog/low-cloud and clear days based on information on large-scale dynamics. The model accurately predicts fog and low-cloud days, illustrating the importance of large-scale pressure modulation and advective processes. It can be concluded that regional fog in the Namib is predominantly of an advective nature and that fog and low-cloud cover is effectively maintained by increased cloud-top radiative cooling. Seasonally different manifestations of synoptic-scale disturbances act to modify its day-to-day variability and the balance of mechanisms leading to its formation and maintenance. The results are the basis for a new conceptual model of the synoptic-scale mechanisms that control fog and low-cloud variability in the Namib Desert and will guide future studies of coastal fog regimes.

Low Cloud Cover Sensitivity to Biomass-Burning Aerosols and Meteorology over the Southeast Atlantic

2018 ◽  
Vol 31 (11) ◽  
pp. 4329-4346 ◽  
Author(s):  
Adeyemi A. Adebiyi ◽  
Paquita Zuidema
Keyword(s):  
Biomass Burning ◽  
Cloud Cover ◽  
Longwave Radiation ◽  
Cloud Fraction ◽  
Specific Humidity ◽  
Aerosol Layer ◽  
Stratocumulus Clouds ◽  
Low Cloud ◽  
Low Cloud Cover ◽  
Absorbing Aerosols

Abstract Shortwave-absorbing aerosols seasonally cover and interact with an expansive low-level cloud deck over the southeast Atlantic. Daily anomalies of the MODIS low cloud fraction, fine-mode aerosol optical depth (AODf), and six ERA-Interim meteorological parameters (lower-tropospheric stability, 800-hPa subsidence, 600-hPa specific humidity, 1000- and 800-hPa horizontal temperature advection, and 1000-hPa geopotential height) are constructed spanning July–October (2001–12). A standardized multiple linear regression, whereby the change in the low cloud fraction to each component’s variability is normalized by one standard deviation, facilitates comparison between the different variables. Most cloud–meteorology relationships follow expected behavior for stratocumulus clouds. Of interest is the low cloud–subsidence relationship, whereby increasing subsidence increases low cloud cover between 10° and 20°S but decreases it elsewhere. Increases in AODf increase cloudiness everywhere, independent of other meteorological predictors. The cloud–AODf effect is partially compensated by accompanying increases in the midtropospheric moisture, which is associated with decreases in low cloud cover. This suggests that the free-tropospheric moisture affects the low cloud deck primarily through longwave radiation rather than mixing. The low cloud cover is also more sensitive to aerosol when the vertical distance between the cloud and aerosol layer is relatively small, which is more likely to occur early in the biomass burning season and farther offshore. A parallel statistical analysis that does not include AODf finds altered relationships between the low cloud cover changes and meteorology that can be understood through the aerosol cross-correlations with the meteorological predictors. For example, the low cloud–stability relationship appears stronger if aerosols are not explicitly included.

scholarly journals The Strength of Low-Cloud Feedbacks and Tropical Climate: A CESM Sensitivity Study

2019 ◽  
Vol 32 (9) ◽  
pp. 2497-2516 ◽  
Author(s):  
Ehsan Erfani ◽  
Natalie J. Burls
Keyword(s):  
Climate Sensitivity ◽  
Cloud Cover ◽  
Hydrological Cycle ◽  
Tropical Climate ◽  
Cloud Feedback ◽  
Cloud Feedbacks ◽  
Feedback Strength ◽  
Low Cloud ◽  
Low Cloud Cover ◽  
The Impact

Abstract Variability in the strength of low-cloud feedbacks across climate models is the primary contributor to the spread in their estimates of equilibrium climate sensitivity (ECS). This raises the question: What are the regional implications for key features of tropical climate of globally weak versus strong low-cloud feedbacks in response to greenhouse gas–induced warming? To address this question and formalize our understanding of cloud controls on tropical climate, we perform a suite of idealized fully coupled and slab-ocean climate simulations across which we systematically scale the strength of the low-cloud-cover feedback under abrupt 2 × CO2 forcing within a single model, thereby isolating the impact of low-cloud feedback strength. The feedback strength is varied by modifying the stratus cloud fraction so that it is a function of not only local conditions but also global temperature in a series of abrupt 2 × CO2 sensitivity experiments. The unperturbed decrease in low cloud cover (LCC) under 2 × CO2 is greatest in the mid- and high-latitude oceans, and the subtropical eastern Pacific and Atlantic, a pattern that is magnified as the feedback strength is scaled. Consequently, sea surface temperature (SST) increases more in these regions as well as the Pacific cold tongue. As the strength of the low-cloud feedback increases this results in not only increased ECS, but also an enhanced reduction of the large-scale zonal and meridional SST gradients (structural climate sensitivity), with implications for the atmospheric Hadley and Walker circulations, as well as the hydrological cycle. The relevance of our results to simulating past warm climate is also discussed.

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.

Sign in / Sign up

Export Citation Format

Share Document