scholarly journals The Psychometric Function: Why we should not, and need not, estimate the lapse rate

2011 ◽  
Vol 11 (11) ◽  
pp. 1175-1175
Author(s):  
N. Prins
Keyword(s):  
Psychometric Function ◽  
Lapse Rate

scholarly journals The psychometric function: The lapse rate revisited

2012 ◽  
Vol 12 (6) ◽  
pp. 25-25 ◽  
Author(s):  
N. Prins
Keyword(s):  
Psychometric Function ◽  
Lapse Rate

scholarly journals On differentiating sensory and attentional contributions to psychophysical performance

2018 ◽  
Author(s):  
Bernt Skottun
Keyword(s):  
Psychometric Function ◽  
Stimulus Intensity ◽  
The Other ◽  
Lapse Rate ◽  
Simple Method ◽  
Other Hand

Both sensory and attentional factors have the ability to influence psychophysical performance. To be ableto differentiate these factors is important in relation to conditions such as, e.g., dyslexia and schizophrenia.A generalized expression of the psychometric function takes account of the ”Lapse Rate”. By assuming theLapse Rate corresponds to ”lapses of attention”, i.e. to inattention so as to make attention = 1.0 - LapseRate, it is possible to generate a simple method for distinguishing attentional and sensory contributions topsychophysical performance. Psychometric functions increase with stimulus intensity, or the magnitude ofstimulus difference, up to an upper asymptote. From the level of this asymptote it is possible to determinethe Lapse Rate and consequently the level of attention. Sensory capabilities, on the other hand, can bededuced from noting where the psychometric function crosses the level halfway between chance performanceand the upper asymptote.

scholarly journals Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

2020 ◽  
Vol 13 (1) ◽  
pp. 113
Author(s):  
Antonio-Juan Collados-Lara ◽  
Steven R. Fassnacht ◽  
Eulogio Pardo-Igúzquiza ◽  
David Pulido-Velazquez
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
National Park ◽  
Rocky Mountain ◽  
Rocky Mountain National Park ◽  
Temperature Fields ◽  
Lapse Rate ◽  
Validation Experiment

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.

A newly developed South American Mapping of Temperature ( SAMeT ) with estimated lapse rate corrections

2021 ◽  
Author(s):  
José Roberto Rozante ◽  
Enver Ramirez Gutierrez ◽  
Alex Almeida Fernandes
Keyword(s):  
Lapse Rate ◽  
South American

The Stratification of the Atmosphere1 (I)

1950 ◽  
Vol 31 (3) ◽  
pp. 71-78 ◽  
Author(s):  
H. Flohn ◽  
R. Penndorf
Keyword(s):  
Thermal Structure ◽  
Mixing Layer ◽  
Sudden Change ◽  
Atomic Layer ◽  
Bottom Layer ◽  
Lapse Rate ◽  
New Classification ◽  
Isothermal Layer ◽  
Outer Atmosphere ◽  
Definition Of

A suitable nomenclature for atmospheric strata as well as a clear definition of the boundaries is proposed. The necessity of such a new classification is stressed. The atmosphere is divided into an inner and an outer atmosphere; from the latter particles may escape. The inner atmosphere is divided into three spheres—troposphere, stratosphere, and ionosphere—with each sphere in turn being subdivided into 3 or 4 layers. The new classification is based upon the thermal structure of the atmosphere.' Boundaries of each layer are fixed by a sudden change of lapse rate. The bottom layer, the ground layer, the advection layer, and the tropopause layer are subdivisions of the troposphere. The advantages gained by defining a separate tropopause layer as part of the troposphere are discussed in detail. Its upper boundary is assumed to be situated at 12 km over temperate latitudes. The stratosphere, consisting of an isothermal layer, a warm layer, and an upper mixing layer, extends from 12 to 80 km. The atmosphere between 80 and 800 km is occupied by the ionosphere, the subdivisions of which are the E-layer, the Flayer and the atomic layer. Above that height the exosphere exists.

scholarly journals Basic Diagnosis and Prediction of Persistent Contrail Occurrence Using High-Resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

2009 ◽  
Vol 48 (9) ◽  
pp. 1790-1802 ◽  
Author(s):  
David P. Duda ◽  
Patrick Minnis
Keyword(s):  
Relative Humidity ◽  
Wind Direction ◽  
Meteorological Data ◽  
Logistic Models ◽  
Satellite Observations ◽  
Lapse Rate ◽  
Prediction System ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Surface Models

Abstract A probabilistic forecast to accurately predict contrail formation over the conterminous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and the Rapid Update Cycle (RUC) combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The most common predictors selected for the SURFACE models tend to be related to temperature, relative humidity, and wind direction when the models are generated using RUC or ARPS analyses. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The most common predictors for the OUTBREAK models tend to be wind direction, atmospheric lapse rate, temperature, relative humidity, and the product of temperature and humidity.

scholarly journals Thermal structure of intense convective clouds derived from GPS radio occultations

2012 ◽  
Vol 12 (12) ◽  
pp. 5309-5318 ◽  
Author(s):  
R. Biondi ◽  
W. J. Randel ◽  
S.-P. Ho ◽  
T. Neubert ◽  
S. Syndergaard
Keyword(s):  
Thermal Structure ◽  
Deep Convection ◽  
Lapse Rate ◽  
Orthogonal Polarization ◽  
Cold Temperatures ◽  
Temperature Lapse Rate ◽  
Convective Systems ◽  
Convective Clouds ◽  
Strong Inversion ◽  
Cloud Tops

Abstract. Thermal structure associated with deep convective clouds is investigated using Global Positioning System (GPS) radio occultation measurements. GPS data are insensitive to the presence of clouds, and provide high vertical resolution and high accuracy measurements to identify associated temperature behavior. Deep convective systems are identified using International Satellite Cloud Climatology Project (ISCCP) satellite data, and cloud tops are accurately measured using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO) lidar observations; we focus on 53 cases of near-coincident GPS occultations with CALIPSO profiles over deep convection. Results show a sharp spike in GPS bending angle highly correlated to the top of the clouds, corresponding to anomalously cold temperatures within the clouds. Above the clouds the temperatures return to background conditions, and there is a strong inversion at cloud top. For cloud tops below 14 km, the temperature lapse rate within the cloud often approaches a moist adiabat, consistent with rapid undiluted ascent within the convective systems.

The psychometric function of infants detecting broadband noise

1998 ◽  
Vol 21 ◽  
pp. 755
Author(s):  
Lynne A. Werner ◽  
Julianne M. Siebens
Keyword(s):  
Psychometric Function ◽  
Broadband Noise

scholarly journals A Two-Stage Quality Control Method for 2-m Temperature Observations Using Biweight Means and a Progressive EOF Analysis

2013 ◽  
Vol 141 (2) ◽  
pp. 798-808 ◽  
Author(s):  
Zhifang Xu ◽  
Yi Wang ◽  
Guangzhou Fan
Keyword(s):  
Quality Control ◽  
Control Method ◽  
Control Process ◽  
Lapse Rate ◽  
Systematic Bias ◽  
Eof Analysis ◽  
Two Stage ◽  
Quality Control Method ◽  
Quality Control Process ◽  
The Difference

Abstract The relatively smooth terrain embedded in the numerical model creates an elevation difference against the actual terrain, which in turn makes the quality control of 2-m temperature difficult when forecast or analysis fields are utilized in the process. In this paper, a two-stage quality control method is proposed to address the quality control of 2-m temperature, using biweight means and a progressive EOF analysis. The study is made to improve the quality control of the observed 2-m temperature collected by China and its neighboring areas, based on the 6-h T639 analysis from December 2009 to February 2010. Results show that the proposed two-stage quality control method can secure the needed quality control better, compared with a regular EOF quality control process. The new method is, in particular, able to remove the data that are dotted with consecutive errors but showing small fluctuations. Meanwhile, compared with the lapse rate of temperature method, the biweight mean method is able to remove the systematic bias generated by the model. It turns out that such methods make the distributions of observation increments (the difference between observation and background) more Gaussian-like, which ensures the data quality after the quality control.

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