Modeling combined global, beam, and diffuse clear-sky indices with Markov-chain mixture distribution models

AbstractWe have investigated whether differences across Clouds and the Earth’s Radiant Energy System (CERES) top-of-atmosphere (TOA) clear-sky angular distribution models, estimated separately over regional (1° × 1° longitude–latitude) and temporal (monthly) bins above land, can be explained by geophysical parameters from Max Planck Institute Aerosol Climatology, version 1 (MAC-v1), ECMWF twentieth-century reanalysis (ERA-20C), and a MODIS bidirectional reflectance distribution function (BRDF)/albedo/nadir BRDF-adjusted reflectance (NBAR) Climate Modeling Grid (CMG) gap-filled products (MCD43GF) climatology. Our research aimed to dissolve binning and to isolate inherent properties or indicators of such properties, which govern the TOA radiance-to-flux conversion in the absence of clouds. We collocated over seven million clear-sky footprints from CERES Single Scanner Footprint (SSF), edition 4, data with above geophysical auxiliary data. Looking at data per surface type and per scattering direction—as perceived by the broadband radiometer (BBR) on board Earth Clouds, Aerosol and Radiation Explorer (EarthCARE)—we identified optimal subsets of geophysical parameters using two different methods: random forest regression followed by a permutation test and multiple linear regression combined with the genetic algorithm. Using optimal subsets, we then trained artificial neural networks (ANNs). Flux error standard deviations on unseen test data were on average 2.7–4.0 W m−2, well below the 10 W m−2 flux accuracy threshold defined for the mission, with the exception of footprints containing fresh snow. Dynamic surface types (i.e., fresh snow and sea ice) required simpler ANN input sets to guarantee mission-worthy flux estimates, especially over footprints consisting of several surface types.

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FUZZY C-MEANS AND MIXTURE DISTRIBUTION MODELS IN THE PRESENCE OF NOISE CLUSTERS

International Journal of Image and Graphics ◽

10.1142/s0219467802000858 ◽

2002 ◽

Vol 02 (04) ◽

pp. 573-586 ◽

Cited By ~ 5

Author(s):

SADAAKI MIYAMOTO ◽

ARNOLD C. ALANZADO

Keyword(s):

Em Algorithm ◽

Statistical Model ◽

Fuzzy Model ◽

Mixture Distribution ◽

Distribution Model ◽

Distribution Models ◽

The Em Algorithm ◽

Mixture Distribution Model ◽

The Relationship ◽

Noise Cluster

This paper discusses the relationship between the K-L information based FCM method and a mixture distribution model with the EM algorithm when a noise cluster is assumed. Equivalence between the two methods in the sense that the derived solutions are the same is proved. From the equivalence a parameter in the fuzzy model can be estimated by using the mixture distribution model. A regularized FCM algorithm with the noise cluster is moreover derived for which the equivalent statistical model with the EM algorithm does not exist.

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A Markov-chain probability distribution mixture approach to the clear-sky index

Solar Energy ◽

10.1016/j.solener.2018.05.055 ◽

2018 ◽

Vol 170 ◽

pp. 174-183 ◽

Cited By ~ 12

Author(s):

Joakim Munkhammar ◽

Joakim Widén

Keyword(s):

Markov Chain ◽

Probability Distribution ◽

Clear Sky ◽

Distribution Mixture

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Evaluating Stochastic Precipitation Generators for Climate Change Impact Studies of New York City’s Primary Water Supply

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0169.1 ◽

2017 ◽

Vol 18 (3) ◽

pp. 879-896 ◽

Cited By ~ 15

Author(s):

Nachiketa Acharya ◽

Allan Frei ◽

Jie Chen ◽

Leslie DeCristofaro ◽

Emmet M. Owens

Keyword(s):

Climate Change ◽

New York ◽

Markov Chain ◽

Water Supply ◽

New York State ◽

Distribution Models ◽

Adaptation Studies ◽

Precipitation Occurrence ◽

Extreme Precipitation Events ◽

The Impact

Abstract Watersheds located in the Catskill Mountains of southeastern New York State contribute about 90% of the water to the New York City water supply system. Recent studies show that this region is experiencing increasing trends in total precipitation and extreme precipitation events. To assess the impact of this and other possible climatic changes on the water supply, there is a need to develop future climate scenarios that can be used as input to hydrological and reservoir models. Recently, stochastic weather generators (SWGs) have been used in climate change adaptation studies because of their ability to produce synthetic weather time series. This study examines the performance of a set of SWGs with varying levels of complexity to simulate daily precipitation characteristics, with a focus on extreme events. To generate precipitation occurrence, three Markov chain models (first, second, and third orders) were evaluated in terms of simulating average and extreme wet days and dry/wet spell lengths. For precipitation magnitude, seven models were investigated, including five parametric distributions, one resampling technique, and a polynomial-based curve fitting technique. The methodology applied here to evaluate SWGs combines several different types of metrics that are not typically combined in a single analysis. It is found that the first-order Markov chain performs as well as higher orders for simulating precipitation occurrence, and two parametric distribution models (skewed normal and mixed exponential) are deemed best for simulating precipitation magnitudes. The specific models that were found to be most applicable to the region may be valuable in bottom-up vulnerability studies for the watershed, as well as for other nearby basins.

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