scholarly journals Investigating diffuse irradiance variation under different cloud conditions in Durban, using k-means clustering

2019 ◽  
Vol 30 (3) ◽  
pp. 22-32
Author(s):  
Paulene Govender ◽  
Venkataraman Sivakumar
Keyword(s):  
Class I ◽  
Cloud Data ◽  
Diffuse Irradiance ◽  
Cloud Dynamics ◽  
Solar Plant ◽  
Low Cloud ◽  
Irradiance Variation ◽  
Solar Powered ◽  
Cloud Patterns ◽  
Sky Conditions

Diffuse irradiance is important for the operation of solar-powered devices such as photovoltaics, so it is important to analyse its behaviour under different sky conditions. The primary cause of short-term irradiance variability is clouds. One approach to analyse the diffuse irradiance variation is to use cluster analysis to group together days experiencing similar cloud patterns. A study was carried out to examine the application of k-means clustering to daily cloud data in Durban, South Africa (29.87 °S; 30.98 °E), which revealed four distinct day-time cloud cover (CC) patterns classified as Class I, II, III and IV, corresponding to cloudy, sunny, or a combination of the two. Diffuse irradiance was then correlated with each of the classes to establish corresponding diurnal irradiance patterns and the associated temporal variation. Class I had highest diffuse irradiance variation, followed by Classes III, IV and II. To further investigate the local cloud dynamics, cloud types were also analysed for Classes I−IV. It was found that stratocumulus (low cloud category); altocumulus translucidus, castellanus and altocumulus (middle cloud category); and cirrus fibrates and spissatus (high cloud category), were the most frequently occurring cloud types within the different classes. This study contributes to the understanding of the diurnal diffuse irradiance patterns under the four most frequently occurring CC conditions in Durban. Overall, knowledge of these CC and associated diffuse irradiance patterns is useful for solar plant operators to manage plant output where, depending on the CC condition, the use of back-up devices may be increased or reduced accordingly.

scholarly journals Regional Assessments of Low Clouds against Large-Scale Stability in CAM5 and CAM-CLUBB Using MODIS and ERA-Interim Reanalysis Data

2015 ◽  
Vol 28 (4) ◽  
pp. 1685-1706 ◽  
Author(s):  
Terence L. Kubar ◽  
Graeme L. Stephens ◽  
Matthew Lebsock ◽  
Vincent E. Larson ◽  
Peter A. Bogenschutz
Keyword(s):  
Large Scale ◽  
Reanalysis Data ◽  
Smooth Transition ◽  
Cloud Data ◽  
Low Clouds ◽  
Community Atmosphere Model ◽  
Northeastern Pacific ◽  
Low Cloud ◽  
Cloud Parameterization ◽  
Static Energy

Abstract Daily gridded cloud data from MODIS and ERA-Interim reanalysis have been assessed to examine variations of low cloud fraction (CF) and cloud-top height and their dependence on large-scale dynamics and a measure of stability. To assess the stratocumulus (Sc) to cumulus (Cu) transition (STCT), the observations are used to evaluate two versions of the NCAR Community Atmosphere Model version 5 (CAM5), both the base model and a version that has implemented a new subgrid low cloud parameterization, Cloud Layers Unified by Binormals (CLUBB). The ratio of moist static energy (MSE) at 700–1000 hPa (MSEtotal) is a skillful predictor of median CF of screened low cloud grids. Values of MSEtotal less than 1.00 represent either conditionally or absolutely unstable layers, and probability density functions of CF suggest a preponderance of either trade Cu (median CF < 0.4) or transitional Sc clouds (0.4 < CF < 0.9). With increased stability (MSEtotal > 1.00), an abundance of overcast or nearly overcast low clouds exists. While both MODIS and ERA-Interim indicate a fairly smooth transition between the low cloud regimes, CAM5-Base simulates an abrupt shift from trade Cu to Sc, with trade Cu covering both too much area and occurring over excessively strong stabilities. In contrast, CAM-CLUBB simulates a smoother trade Cu to Sc transition (CTST) as a function of MSEtotal, albeit with too extensive coverage of overcast Sc in the primary northeastern Pacific subsidence region. While the overall CF distribution in CAM-CLUBB is more realistic, too few transitional clouds are simulated for intermediate MSEtotal compared to observations.

scholarly journals Comparison of Satellite Cloud Masks with Ceilometer Sky Conditions in Southern Finland

2010 ◽  
Vol 49 (12) ◽  
pp. 2508-2526 ◽  
Author(s):  
Sauli Joro ◽  
Otto Hyvärinen ◽  
Janne Kotro
Keyword(s):  
Early Morning ◽  
Computational Method ◽  
Cloud Detection ◽  
Continuous Quality ◽  
Infrared Imager ◽  
Observation Network ◽  
Low Cloud ◽  
Sky Conditions ◽  
Cloud Mask ◽  
Better Than

Abstract The cloud mask is an essential product derived from satellite data. Whereas cloud analysis applications typically make use of information from cloudy pixels, many other applications require cloud-free conditions. For this reason many organizations have their own cloud masks tuned to serve their particular needs. Being a fundamental product, continuous quality monitoring and validation of these cloud masks are vital. This study evaluated the performance of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteorological Products Extraction Facility cloud mask (MPEF), together with the Nowcasting Satellite Application Facility (SAFNWC) cloud masks provided by Météo-France (SAFNWC/MSG) and the Swedish Meteorological and Hydrological Institute (SAFNWC/PPS), in the high-latitude area of greater Helsinki in Finland. The first two used the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument from the geostationary Meteosat-8 satellite, whereas the last used the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the polar-orbiting NOAA satellite series. Ceilometer data from the Helsinki Testbed, an extensive observation network covering the greater Helsinki area in Finland, were used as reference data in the cloud mask comparison. A computational method, called bootstrapping, is introduced to account for the strong temporal and spatial correlation of the ceilometer observations. The method also allows the calculation of the confidence intervals (CI) for the results. This study comprised data from February and August 2006. In general, the SAFNWC/MSG algorithm performed better than MPEF. Differences were found especially in the early morning low cloud detection. The SAFNWC/PPS cloud mask performed very well in August, better than geostationary-based masks, but had problems in February when its performance was worse. The use of the CIs gave the results more depth, and their use should be encouraged.

A New Method for Correcting Shadow Band Diffuse Irradiance Data

2001 ◽  
Vol 124 (1) ◽  
pp. 34-43 ◽  
Author(s):  
T. Muneer ◽  
X. Zhang
Keyword(s):  
Beam Energy ◽  
Polar Axis ◽  
Site Specific ◽  
Beer Sheva ◽  
Diffuse Irradiance ◽  
Proposed Model ◽  
Irradiance Data ◽  
Comparison Of The Results ◽  
Sky Conditions ◽  
Diffuse Distribution

An instrument commonly used to measure diffuse irradiance is the polar-axis shadow band pyranometer. However, the shadow band that is used to prevent the beam energy from entering the pyranometer also obscures part of sky-diffuse irradiance. A correction factor must hence be applied to obtain as accurate as possible the estimation of the true diffuse irradiance. In this article, the development of a new model based on an anisotropic sky-diffuse distribution theory is presented. The proposed model is validated using two databases from different sites with various sky conditions. Drummond’s method, which is based on geometrical calculation, is also examined using the same databases. Comparison of the results obtained through application of the proposed model, with those generated by Drummond’s method shows that, for the case of Bracknell, UK the proposed method gives a root mean square error (RMSE) of 12 W/m2, as compared to Drummond’s figure of 16 W/m2. For the case of Beer Sheva, Israel the proposed model produces an RMSE of 17 W/m2, while Drummond’s procedure results in 23 W/m2. It has been demonstrated herein that the proposed method is not site specific.

scholarly journals Boundary Layer and Cloud Structure Controls on Tropical Low Cloud Cover Using A-Train Satellite Data and ECMWF Analyses

2011 ◽  
Vol 24 (1) ◽  
pp. 194-215 ◽  
Author(s):  
Terence L. Kubar ◽  
Duane E. Waliser ◽  
J-L. Li
Keyword(s):  
Boundary Layer ◽  
Cross Section ◽  
Large Scale ◽  
Layer Structure ◽  
Deep Convection ◽  
Static Stability ◽  
Boundary Layer Stability ◽  
Middle Point ◽  
Cloud Data ◽  
Low Cloud

Abstract The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), CloudSat radar, and the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud data on the A-Train constellation complemented with the European Centre for Medium-Range Forecasts (ECMWF) analyses are used to investigate the cloud and boundary layer structure across a 10° wide cross section starting at 5°S near the international date line and extending to 35°N near the California coast from March 2008 to February 2009. The mean large-scale inversion height and low-level cloud tops, which correspond very closely to each other, are very shallow (∼500 m) over cold SSTs and high static stability near California and deepen southwestward (to a maximum of ∼1.5–2.0 km) along the cross section as SSTs rise. Deep convection near the ITCZ occurs at a surface temperature close to 298 K. While the boundary layer relative humidity (RH) is nearly constant where a boundary layer is well defined, it drops sharply near cloud top in stratocumulus regions, corresponding with strong thermal inversions and water vapor decrease, such that the maximum (−∂RH/∂z) marks the boundary layer cloud top very well. The magnitude correlates well with low cloud frequency during March–May (MAM), June–August (JJA), and September–November (SON) (r 2 = 0.85, 0.88, and 0.86, respectively). Also, CALIPSO and MODIS isolated low cloud frequency generally agree quite well, but CloudSat senses only slightly more than one-third of the low clouds as observed by the other sensors, as many clouds are shallower than 1 km and thus cannot be discerned with CloudSat due to contamination from the strong signal from surface clutter. Mean tropospheric ω between 300 and 700 hPa is examined from the ECMWF Year of Tropical Convection (YOTC) analysis dataset, and during JJA and SON, strong rising motion in the middle troposphere is confined to a range of 2-m surface temperatures between 297 and 300 K, consistent with previous studies that show a narrow range of SSTs over which deep ascent occurs. During December–February (DJF), large-scale ascending motion extends to colder SSTs and high boundary layer stability. A slightly different boundary layer stability metric is derived, the difference of moist static energy (MSE) at the middle point of the inversion (or at 700 hPa if no inversion exists) and the surface, referred to as ΔMSE. The utility of ΔMSE is its prediction of isolated uniform low cloud frequency, with very high r 2 values of 0.93 and 0.88, respectively, for the MODIS and joint lidar plus radar product during JJA but significantly lower values during DJF (0.46 and 0.40), with much scatter. To quantify the importance of free tropospheric dynamics in modulating the ΔMSE–low cloud relationships, the frequency as a function of ΔMSE of rising motion profiles (ω < −0.05 Pa s−1) is added to the observed low cloud frequency for a maximum hypothetical low cloud frequency. Doing this greatly reduces the interseasonal differences and holds promise for using ΔMSE for parameterization schemes and examining low cloud feedbacks.

scholarly journals On the Annual Cycle, Variability, and Correlations of Oceanic Low-Topped Clouds with Large-Scale Circulation Using Aqua MODIS and ERA-Interim

2012 ◽  
Vol 25 (18) ◽  
pp. 6152-6174 ◽  
Author(s):  
Terence L. Kubar ◽  
Duane E. Waliser ◽  
J.-L. Li ◽  
Xianan Jiang
Keyword(s):  
Pacific Northwest ◽  
Annual Cycle ◽  
Time Scale ◽  
Large Scale ◽  
Correlation Coefficients ◽  
Tropospheric Temperature ◽  
Strong Suppression ◽  
Cloud Data ◽  
Low Cloud ◽  
Averaging Time

Abstract Eight years of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) level-3 cloud data in conjunction with collocated Interim ECMWF Re-Analysis are used to investigate relationships between isolated low-topped cloud fraction (LCF) and dynamics/thermodynamics versus averaging time scale. Correlation coefficients between LCF and −SST exceed 0.70 over 56% of ocean regions from 25°S to 25°N for 90-day running means and exceed 0.70 between LCF and 500-hPa omega (ω500) for over one-third of oceans from 50°S to 50°N. Correlations increase most dramatically by increasing the averaging time scale from 1 day to about 15, owing to the large LCF synoptic variability and random effects that are suppressed by averaging. In five regions selected with monthly mean SSTs between 291 and 303 K, SST decreases by −0.13 K %-1 low-cloud cover increase. Monthly LCF is also correlated with estimated inversion strength (EIS), which is SST dominated in low latitudes and free tropospheric temperature dominated in the northeast Atlantic, Pacific, and midlatitudes, though SST and stability are poor predictors of LCF over the southern oceans. Where the fraction of variance explained by the annual LCF harmonic is high, maximum LCF tends to lead minimum SST by ~15–30 days such that clouds can amplify the SST annual cycle, especially when LCF maxima coexist with insolation minima. Monthly mean LCF tends to scale with ω500 exponentially over the convective margins and offshore of the Pacific Northwest, but daily climatology relationships indicate that LCF levels off and even diminishes for ω500 > 0.05 Pa s−1, suggesting a limit through, perhaps, a too strong suppression of boundary layer heights. This suggests the need for dynamic-regime analysis in diagnosing low cloud/circulation feedbacks.

scholarly journals Dry Bias in Satellite-Derived Clear-Sky Water Vapor and Its Contribution to Longwave Cloud Radiative Forcing

2006 ◽  
Vol 19 (21) ◽  
pp. 5570-5580 ◽  
Author(s):  
Byung-Ju Sohn ◽  
Johannes Schmetz ◽  
Rolf Stuhlmann ◽  
Joo-Young Lee
Keyword(s):  
Water Vapor ◽  
Radiative Forcing ◽  
Active Regions ◽  
Tropical Regions ◽  
Cloud Radiative Forcing ◽  
Cloud Data ◽  
Clear Sky ◽  
Cloud Development ◽  
Sampling Problem ◽  
Sky Conditions

Abstract In this paper, the amount of satellite-derived longwave cloud radiative forcing (CRF) that is due to an increase in upper-tropospheric water vapor associated with the evolution from clear-sky to the observed all-sky conditions is assessed. This is important because the satellite-derived clear-sky outgoing radiative fluxes needed for the CRF determination are from cloud-free areas away from the cloudy regions in order to avoid cloud contamination of the clear-sky fluxes. However, avoidance of cloud contamination implies a sampling problem as the clear-sky fluxes represent an area drier than the hypothetical clear-sky humidity in cloudy regions. While this issue has been recognized in earlier works this study makes an attempt to quantitatively estimate the bias in the clear-sky longwave CRF. Water vapor amounts in the 200–500-mb layer corresponding to all-sky condition are derived from microwave measurements with the Special Sensor Microwave Temperature-2 Profiler and are used in combination with cloud data for determining the clear-sky water vapor distribution of that layer. The obtained water vapor information is then used to constrain the humidity profiles for calculating clear-sky longwave fluxes at the top of the atmosphere. It is shown that the clear-sky moisture bias in the upper troposphere can be up to 40%–50% drier over convectively active regions. Results indicate that up to 12 W m−2 corresponding to about 15% of the satellite-derived longwave CRF in tropical regions can be attributed to the water vapor changes associated with cloud development.

Classification and Evaluation of Stable and Unstable Cloud Forecasts

2021 ◽  
Author(s):  
Jason E. Nachamkin ◽  
Adam Bienkowski ◽  
Rich Bankert ◽  
Krishna Pattipati ◽  
David Sidoti ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Cloud Formation ◽  
Cloud Base ◽  
Forecast Errors ◽  
Short Term ◽  
Coverage Bias ◽  
Low Cloud ◽  
Low Cloud Cover ◽  
Cloud Tops ◽  
Cloud Patterns

AbstractA physics-based cloud identification scheme, originally developed for a machine learning forecast system, was applied to verify cloud location and coverage bias errors from two years of 6-hour forecasts. The routine identifies stable and unstable environments based on the potential for buoyant versus stable cloud formation. The efficacy of the scheme is documented by investigating its ability to identify cloud patterns and systematic forecast errors. Results showed stable cloud forecasts contained widespread, persistent negative cloud cover biases most likely associated with turbulent, radiative and microphysical feedback processes. In contrast, unstable clouds were better predicted despite being poorly resolved. This suggests that scale aliasing, while energetically problematic, results in less severe short-term cloud cover errors.This study also evaluated Geostationary Operational Environmental Satellite (GOES) cloud base retrievals for their effectiveness at identifying regions of lower tropospheric cloud cover. Retrieved cloud base heights were sometimes too high with respect to their actual values in regions of deep-layered clouds, resulting in underestimates of the extent of low cloud cover in these areas. Sensitivity experiments indicate the most accurate cloud base estimates existed in regions with cloud tops at or below 8 km.

Electron microscopy analysis of degenerating pancreatic ß cells in transgenic mice expressing the MHC Class I antigen Dd

1990 ◽  
Vol 48 (3) ◽  
pp. 548-549
Author(s):  
T. A. Stewart ◽  
D. Liggitt ◽  
S. Pitts ◽  
L. Martin ◽  
M. Siegel ◽  
...  
Keyword(s):  
Type I Diabetes ◽  
Disease Process ◽  
Class Ii ◽  
Class I ◽  
Type I ◽  
Aberrant Expression ◽  
Mhc Molecules ◽  
Endogenous Insulin ◽  
Class Ii Mhc ◽  
Ifn Γ

Insulin-dependant (Type I) diabetes mellitus (IDDM) is a metabolic disorder resulting from the lack of endogenous insulin secretion. The disease is thought to result from the autoimmune mediated destruction of the insulin producing ß cells within the islets of Langerhans. The disease process is probably triggered by environmental agents, e.g. virus or chemical toxins on a background of genetic susceptibility associated with particular alleles within the major histocompatiblity complex (MHC). The relation between IDDM and the MHC locus has been reinforced by the demonstration of both class I and class II MHC proteins on the surface of ß cells from newly diagnosed patients as well as mounting evidence that IDDM has an autoimmune pathogenesis. In 1984, a series of observations were used to advance a hypothesis, in which it was suggested that aberrant expression of class II MHC molecules, perhaps induced by gamma-interferon (IFN γ) could present self antigens and initiate an autoimmune disease. We have tested some aspects of this model and demonstrated that expression of IFN γ by pancreatic ß cells can initiate an inflammatory destruction of both the islets and pancreas and does lead to IDDM.

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