MÉTODO DA COMPOSTEIRA (BIN METHOD) PARA COMPOSTAGEM DE CARCAÇAS DE ANIMAIS EM CATALÃO

Abstract Information about microphysical processes in warm clouds embedded in satellite measurements must be untangled to be used to improve the parameterization in global models. In this paper, the relationship between vertical profiles of horizontally averaged radar reflectivity Zm and cloud optical depth from cloud top τd was investigated using a hybrid cloud microphysical model and a forward simulator of satellite measurements. The particle size distributions were explicitly simulated using a bin method in a kinematic framework. In contrast to previous interpretations of satellite-observed data, three patterns of the Zm–τd relationship related to microphysical processes were identified. The first is related to the autoconversion process, which causes Zm to increase upward with decreasing τd. Before the initiation of surface precipitation, Zm increases downward with τd in the upper part of the cloud, which is considered to be a second characteristic pattern and is caused by the accretion process. The appearance of this pattern corresponds to the initiation of efficient production of raindrops in the cloud. The third is related to the sedimentation and evaporation of raindrops causing Zm to decrease downward with τd in the lower part of the Zm–τd relationship. It was also found that the bulk collection efficiency has a partially positive correlation with the slope factor of Zm with regard to τd and that the slope factor could be a gross measure of the collection efficiency in partial cases. This study also shows that differences in the aerosol concentration modulate the duration of these three patterns and change the slope factor of Zm.

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The ‘bin’ method—A procedure for predicting seasonal energy requirements in buildings

Building Services Engineering Research and Technology ◽

10.1177/014362448600700201 ◽

1986 ◽

Vol 7 (2) ◽

pp. 55-64 ◽

Cited By ~ 3

Author(s):

K. M. Letherman ◽

J. Dewsbury ◽

G. Birch

Keyword(s):

Energy Requirements ◽

Bin Method

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EVALUATION OF A HEATING SYSTEM WITH A HEAT PUMP ACCORDING TO THE MODE OF INPUT DATA

Czech Journal of Civil Engineering ◽

10.51704/cjce.2015.vol1.iss2.pp65-71 ◽

2015 ◽

Vol 1 (2) ◽

pp. 65-71

Author(s):

Vladimíra Linhartová

Keyword(s):

Heat Pump ◽

Air Temperature ◽

Input Data ◽

Heating System ◽

Temperature Data ◽

Air Source Heat Pump ◽

Air Temperatures ◽

The Difference ◽

Bin Method

The paper is focused on evaluating a heating system with an air source heat pump using the bin method. The main goal of the paper is to find the difference between three modes of input outside air temperature data in the calculation. Outside air temperatures are used in three modes, an hour based calculation, monthly frequencies and annual frequencies based calculations.

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Development of a cloud microphysical model and parameterizations to describe the effect of CCN on warm cloud

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-6-1413-2006 ◽

2006 ◽

Vol 6 (1) ◽

pp. 1413-1453 ◽

Cited By ~ 2

Author(s):

N. Kuba ◽

Y. Fujiyoshi

Keyword(s):

Size Distribution ◽

Droplet Size ◽

Cloud Model ◽

Cloud Droplet ◽

Droplet Size Distribution ◽

Global Model ◽

Lagrangian Framework ◽

Cloud Droplet Size Distribution ◽

Grid Points ◽

Bin Method

Abstract. First, a hybrid cloud microphysical model was developed that incorporates both Lagrangian and Eulerian frameworks to study quantitatively the effect of cloud condensation nuclei (CCN) on the precipitation of warm clouds. A parcel model and a grid model comprise the cloud model. The condensation growth of CCN in each parcel is estimated in a Lagrangian framework. Changes in cloud droplet size distribution arising from condensation and coalescence are calculated on grid points using a two-moment bin method in a semi-Lagrangian framework. Sedimentation and advection are estimated in the Eulerian framework between grid points. Results from the cloud model show that an increase in the number of CCN affects both the amount and the location of precipitation. Additionally, results from the hybrid microphysical model and Kessler's parameterization were compared. Second, new parameterizations were developed that estimate the number and size distribution of cloud droplets given the updraft velocity and the number of CCN. The parameterizations were derived from the results of numerous numerical experiments that used the cloud microphysical parcel model. The input information of CCN for these parameterizations is only several values of CCN spectrum (they are given by CCN counter for example). It is more convenient than conventional parameterizations those need values concerned with CCN spectrum, C and k in the equation of N=CSk, or, breadth, total number and median radius, for example. The new parameterizations' predictions of initial cloud droplet size distribution for the bin method were verified by using the aforesaid hybrid microphysical model. The newly developed parameterizations will save computing time, and can effectively approximate components of cloud microphysics in a non-hydrostatic cloud model. The parameterizations are useful not only in the bin method in the regional cloud-resolving model but also both for a two-moment bulk microphysical model and for a global model. The effects of sea salt, sulfate, and organic carbon particles were also studied with these parameterizations and global model.

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THE BULK BIN METHOD OF HANDLING FRUIT.: I. COOLING AND BRUISING OF McINTOSH APPLES

Canadian Journal of Plant Science ◽

10.4141/cjps58-070 ◽

1958 ◽

Vol 38 (4) ◽

pp. 440-444

Author(s):

A. D. McMechan ◽

D. V. Fisher

Keyword(s):

Cold Storage ◽

Good Condition ◽

Floor Level ◽

Rate Of Cooling ◽

Bin Method

Experiments were conducted to compare the rate of cooling and amount of bruising and stem puncturing of apples handled in 25-bushel bulk bins, and in conventional bushel boxes.Fruit in bins with no facilities for ventilation cooled more slowly than in bushel boxes; fruit in bins with a 1-inch opening on all sides of the bin, at floor level, cooled almost as quickly as in bushel boxes.McIntosh apples in good condition, dumped from the containers soon after being picked, suffered less bruising when handled in bins than when handled in boxes. There was no difference in the amount of stem puncturing caused by the two methods. When the apples were stored for 7 days without refrigeration before being dumped from the containers, there was no difference between the methods in the occurrence of bruising but there were more stem punctures in the bin-handled fruit. When the apples were held in cold storage for 36 days before being dumped, there was no difference between the methods, in either bruising or stem puncturing.

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Bias, Information, Noise: The BIN Model of Forecasting

Management Science ◽

10.1287/mnsc.2020.3882 ◽

2021 ◽

Author(s):

Ville A. Satopää ◽

Marat Salikhov ◽

Philip E. Tetlock ◽

Barbara Mellers

Keyword(s):

Probabilistic Reasoning ◽

Predictive Accuracy ◽

R Package ◽

Intelligence Community ◽

What Works ◽

Wide Range ◽

Efficient Extraction ◽

Bin Method ◽

Underlying Processes ◽

Valid Information

A four-year series of subjective probability forecasting tournaments sponsored by the U.S. intelligence community revealed a host of replicable drivers of predictive accuracy, including experimental interventions such as training in probabilistic reasoning, anti‐groupthink teaming, and tracking of talent. Drawing on these data, we propose a Bayesian BIN model (Bias, Information, Noise) for disentangling the underlying processes that enable forecasters and forecasting methods to improve—either by tamping down bias and noise in judgment or by ramping up the efficient extraction of valid information from the environment. The BIN model reveals that noise reduction plays a surprisingly consistent role across all three methods of enhancing performance. We see the BIN method as useful in focusing managerial interventions on what works when and why in a wide range of domains. An R-package called BINtools implements our method and is available on the first author’s personal website. This paper was accepted by Manel Baucells, decision analysis.

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