scholarly journals On the decadal increase in the tropical mean outgoing longwave radiation for the period 1984-2000

2004 ◽  
Vol 4 (5) ◽  
pp. 1419-1425 ◽  
Author(s):  
D. Hatzidimitriou ◽  
I. Vardavas ◽  
K. G. Pavlakis ◽  
N. Hatzianastassiou ◽  
C. Matsoukas ◽  
...  
Keyword(s):  
Time Series ◽  
Outgoing Longwave Radiation ◽  
Cloud Cover ◽  
Longwave Radiation ◽  
Atmospheric Temperature ◽  
Tropical Region ◽  
Transfer Model ◽  
Cloud Data ◽  
Low Level ◽  
High Level

Abstract. In the present paper, we have calculated the outgoing longwave radiation at the top of the atmosphere (OLR at TOA) using a deterministic radiation transfer model, cloud data from ISCCP-D, and atmospheric temperature and humidity data from NCEP/NCAR reanalysis, for the seventeen-year period 1984-2000. We constructed anomaly time-series of the OLR at TOA, as well as of all of the key input climatological data, averaged in the tropical region between 20°N and 20°S. We compared the anomaly time-series of the model calculated OLR at TOA with that obtained from the ERBE S-10N (WFOV NF edition 2) non-scanner measurements. The model results display very similar seasonal and inter-annual variability as the ERBS data, and indicate a decadal increase of OLR at TOA of 1.9±0.2Wm-2/decade, which is lower than that displayed by the ERBS time-series (3.5±0.3Wm-2). Analysis of the inter-annual and long-term variability of the various parameters determining the OLR at TOA, showed that the most important contribution to the observed trend comes from a decrease in high-level cloud cover over the period 1984-2000, followed by an apparent drying of the upper troposphere and a decrease in low-level cloudiness. Opposite but small trends are introduced by a decrease in low-level cloud top pressure, an apparent cooling of the lower stratosphere (at the 50mbar level) and a small decadal increase in mid-level cloud cover.

scholarly journals On the decadal increase in the tropical mean outgoing longwave radiation for the period 1984–2000

2004 ◽  
Vol 4 (3) ◽  
pp. 2727-2745
Author(s):  
D. Hatzidimitriou ◽  
I. Vardavas ◽  
K. G. Pavlakis ◽  
N. Hatzianastassiou ◽  
C. Matsoukas ◽  
...  
Keyword(s):  
Time Series ◽  
Outgoing Longwave Radiation ◽  
Cloud Cover ◽  
Longwave Radiation ◽  
Atmospheric Temperature ◽  
Tropical Region ◽  
Transfer Model ◽  
Cloud Data ◽  
Low Level ◽  
High Level

Abstract. In the present paper, we have calculated the outgoing longwave radiation at the top of the atmosphere (OLR at TOA) using a deterministic radiation transfer model, cloud data from ISCCP-D, and atmospheric temperature and humidity data from NCEP/NCAR reanalysis, for the seventeen-year period 1984–2000. We constructed anomaly time-series of the OLR at TOA, as well as of all of the key input climatological data, averaged in the tropical region between 20° N and 20° S. We compared the anomaly time-series of the model calculated OLR at TOA with that obtained from the ERBE S-10N (WFOV NF edition 2) non-scanner measurements. The model results display very similar seasonal and inter-annual variability as the ERBS data, and indicate a decadal increase of OLR at TOA of 1.9±0.2 Wm−2/decade, which is lower than that displayed by the ERBS time-series (3.5±0.3 Wm−2). Analysis of the inter-annual and long-term variability of the various parameters determining the OLR at TOA, showed that the most important contribution to the observed trend comes from a decrease in high-level cloud cover over the period 1984–2000, followed by an apparent drying of the upper troposphere and a decrease in low-level cloudiness. Opposite but small trends are introduced by a decrease in low-level cloud top pressure, an apparent cooling of the lower stratosphere (at the 50 mbar level) and a small decadal increase in mid-level cloud cover.

scholarly journals Comparison of the Long-Range Climate Memory in Outgoing Longwave Radiation over the Tibetan Plateau and the Indian Monsoon Region

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Zhongping Shen ◽  
Jun Shi ◽  
Yadong Lei
Keyword(s):  
Time Series ◽  
Outgoing Longwave Radiation ◽  
Cloud Cover ◽  
Indian Monsoon ◽  
Longwave Radiation ◽  
The Tibetan Plateau ◽  
Monsoon Region ◽  
Scaling Exponents ◽  
High Cloud ◽  
Indian Monsoon Region

Based on the detrended fluctuation analysis (DFA) method, scaling behaviors of the daily outgoing longwave radiation (OLR) from 1979 to 2015 over the Tibetan Plateau (TP) and the Indian Monsoon Region (IMR) are analyzed. The results show that there is long-term memory for the OLR time series over the TP and IMR. The long-range memory behaviors of OLR over TP are stronger than those over IMR. The averaged values of the scaling exponents over TP and IMR are 0.71 and 0.64; the maximum values in the two regions are 0.81 and 0.75; the minimum values are 0.59 and 0.58. The maximum frequency counts for scaling exponents occur in the range of 0.625 and 0.675 both in TP and in IMR. The spatial distribution of the scaling exponents of the OLR sequence is closely related to the conditions of climatic high cloud cover in the two areas. The high cloud cover over TP is obviously less than that of IMR. In addition, the scaling behaviors of OLR over TP and IMR are caused by the fractal characteristics of time series, which is further proved by randomly disrupting the time series to remove trends and correlation.

scholarly journals The Relationship Between Cloud Cover and Outgoing Longwave Radiation in the Southern of Vietnam

2018 ◽  
Vol 34 (1S) ◽  
Author(s):  
Chu Thi Thu Huong ◽  
Bui Thi Hop ◽  
Tran Dinh Linh ◽  
Vu Thanh Hang
Keyword(s):  
Spatial Distribution ◽  
Rainy Season ◽  
Outgoing Longwave Radiation ◽  
Cloud Cover ◽  
Temporal Evolution ◽  
El Nino ◽  
Longwave Radiation ◽  
Downward Trend ◽  
La Nina ◽  
The Relationship

Abstract: Based on the data that has the resolution is 1,00×1,00of the Outgoing Longwave Radiation (OLR) and the cloud cover from NCEP/NCAR in the 1981 – 2012 period, the relationship between the cloud cover and the OLR in the Southern of Vietnam wasinvestigated when analyze and compare the spatial distribution, temporal evolution and their correlation. The results show that the characteristics of the spatial distribution and the year cycle of cloud cover and OLR are inversely correlated. The region or time that the cloud cover is great, the OLR is small and vice versa. In the Southern of Vietnam, the OLR value isgreatest(or smallest) in the dry (or rainy) season and in the El-Nino (La-Nina) years. In addition, during the 1981-2012period, the OLR in this region shows a downward trend about 3.6 W/m2/decade, while the cloud cover tends to increase by 0.2%/decade. Keywords: Cloud cover, Outgoing Longwave Radiation, the Southern of Vietnam.

scholarly journals Development of the HIRS Outgoing Longwave Radiation Climate Dataset

2007 ◽  
Vol 24 (12) ◽  
pp. 2029-2047 ◽  
Author(s):  
Hai-Tien Lee ◽  
Arnold Gruber ◽  
Robert G. Ellingson ◽  
Istvan Laszlo
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Outgoing Longwave Radiation ◽  
Longwave Radiation ◽  
Radiation Budget ◽  
Validation Data ◽  
The Earth ◽  
Data Record ◽  
Earth Radiation Budget ◽  
Earth Radiation

Abstract The Advanced Very High Resolution Radiometer (AVHRR) outgoing longwave radiation (OLR) product, which NOAA has been operationally generating since 1979, is a very long data record that has been used in many applications, yet past studies have shown its limitations and several algorithm-related deficiencies. Ellingson et al. have developed the multispectral algorithm that largely improved the accuracy of the narrowband-estimated OLR as well as eliminated the problems in AVHRR. NOAA has been generating High Resolution Infrared Radiation Sounder (HIRS) OLR operationally since September 1998. In recognition of the need for a continuous and long OLR data record that would be consistent with the earth radiation budget broadband measurements in the National Polar-orbiting Operational Environmental Satellite System (NPOESS) era, and to provide a climate data record for global change studies, a vigorous reprocessing of the HIRS radiance for OLR derivation is necessary. This paper describes the development of the new HIRS OLR climate dataset. The HIRS level 1b data from the entire Television and Infrared Observation Satellite N-series (TIROS-N) satellites have been assembled. A new radiance calibration procedure was applied to obtain more accurate and consistent HIRS radiance measurements. The regression coefficients of the HIRS OLR algorithm for all satellites were rederived from calculations using an improved radiative transfer model. Intersatellite calibrations were performed to remove possible discontinuity in the HIRS OLR product from different satellites. A set of global monthly diurnal models was constructed consistent with the HIRS OLR retrievals to reduce the temporal sampling errors and to alleviate an orbital-drift-induced artificial trend. These steps significantly improved the accuracy, continuity, and uniformity of the HIRS monthly mean OLR time series. As a result, the HIRS OLR shows a comparable stability as in the Earth Radiation Budget Satellite (ERBS) nonscanner OLR measurements. HIRS OLR has superb agreement with the broadband observations from Earth Radiation Budget Experiment (ERBE) and Clouds and the Earth’s Radiant Energy System (CERES) in the ENSO-monitoring regions. It shows compatible ENSO-monitoring capability with the AVHRR OLR. Globally, HIRS OLR agrees with CERES with an accuracy to within 2 W m−2 and a precision of about 4 W m−2. The correlation coefficient between HIRS and CERES global monthly mean is 0.997. Regionally, HIRS OLR agrees with CERES to within 3 W m−2 with precisions better than 3 W m−2 in most places. HIRS OLR could be used for constructing climatology for applications that plan to use NPOESS ERBS and previously used AVHRR OLR observations. The HIRS monthly mean OLR data have high accuracy and precision with respect to the broadband observations of ERBE and CERES. It can be used as an independent validation data source. The uniformity and continuity of HIRS OLR time series suggest that it could be used as a reliable transfer reference for the discontinuous broadband measurements from ERBE, CERES, and ERBS.

scholarly journals PRODUCTION, CONSUMPTION AND PRICE (IMPORTS, PRODUCERS AND CONSUMER) VOLATILITY OF SHALLOT IN PROBOLINGGO REGENCY

2021 ◽  
Vol 21 (3) ◽  
pp. 235-240
Author(s):  
Susanti Evie Sulistiowati ◽  
Ratya Anindita ◽  
Rosihan Asmara
Keyword(s):  
Time Series ◽  
Secondary Data ◽  
Risk And Uncertainty ◽  
Producer Price ◽  
Import Prices ◽  
Low Level ◽  
Consumption Volatility ◽  
Consumer Prices ◽  
Production And Consumption ◽  
High Level

Changes in production, consumption, and import variables cause the price of shallots to fluctuate. A high and unpredictable price fluctuation increasing the volatility problem. This phenomenon gives results in risk, uncertainty and leading to a decline in the welfare of producers and consumers. Based on the description of the problems above, it is important to analyze price, production, import, and consumption volatility to determined the level of risk and uncertainty faced by producers and consumers. This study uses monthly secondary data (time series) on production, price, imports, and consumption of shallots in Probolinggo Regency, for seven years (2013-2019). The ARCH/GARCH method is used to analyze the volatility of prices, production and consumption. The results from the analysis are the production variable has a low level of volatility, the consumption and import prices have high-level volatility, producer price has low-level volatility, while consumer prices has-high level volatility. From the results means that the risks and uncertainties faced by producers in conducting shallot cultivation are low. While for the consumer means the risks and uncertainties in consuming shallots are high.

scholarly journals Detecting Thermal Anomalies of Earthquake Process Within Outgoing Longwave Radiation Using Time Series Forecasting Models

2020 ◽  
Vol 63 (5) ◽  
Author(s):  
Dulin Zhai ◽  
Xueming Zhang ◽  
Pan Xiong
Keyword(s):  
Time Series ◽  
Outgoing Longwave Radiation ◽  
Time Series Data ◽  
Arima Model ◽  
Longwave Radiation ◽  
Time Series Forecasting ◽  
Series Data ◽  
Jiuzhaigou Earthquake ◽  
Forecasting Models ◽  
Mexico Earthquake

  The catastrophic damages caused by the Jiuzhaigou earthquake in China of August 8, 2017 and the Mexico earthquake of September 20, 2017 have revealed some important weaknesses of currently operational earthquake-monitoring and forecasting systems. In this work, six time series forecasting models were applied to detect pre-earthquake anomalies within infrared outgoing longwave radiation. After comparing their prediction results using non-seismic time series data, the autoregressive integrated moving average (ARIMA) model was selected as the optimal model, and then a new prediction method based on this ARIMA model was proposed. The results show that the values observed on July 27 and August 5 before the Jiuzhaigou earthquake in China exceed the confidence interval for prediction and reaches the maximum on August 5, 2017. This indicates the infrared outgoing longwave radiation (IR-OLR) anomalies before the Jiuzhaigou earthquake in China. For the Mexico earthquake, pre-earthquake IR-OLR anomalies are detected on September 14, 18, and 19, and reaches the maximum on September 14, 2017. This demonstrates that the proposed time series forecasting model based on ARIMA could be an effective method for earthquake anomalies detection within infrared outgoing longwave radiation.

Arctic Cloud Changes from Surface and Satellite Observations

2010 ◽  
Vol 23 (15) ◽  
pp. 4233-4242 ◽  
Author(s):  
Ryan Eastman ◽  
Stephen G. Warren
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Cloud Cover ◽  
The Arctic ◽  
Interannual Variations ◽  
Infrared Observation ◽  
Cloud Data ◽  
Surface Data ◽  
Avhrr Data ◽  
Very High

Abstract Visual cloud reports from land and ocean regions of the Arctic are analyzed for total cloud cover. Trends and interannual variations in surface cloud data are compared to those obtained from Advanced Very High Resolution Radiometer (AVHRR) and Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) satellite data. Over the Arctic as a whole, trends and interannual variations show little agreement with those from satellite data. The interannual variations from AVHRR are larger in the dark seasons than in the sunlit seasons (6% in winter, 2% in summer); however, in the surface observations, the interannual variations for all seasons are only 1%–2%. A large negative trend for winter found in the AVHRR data is not seen in the surface data. At smaller geographic scales, time series of surface- and satellite-observed cloud cover show some agreement except over sea ice during winter. During the winter months, time series of satellite-observed clouds in numerous grid boxes show variations that are strangely coherent throughout the entire Arctic.

scholarly journals Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

2008 ◽  
Vol 23 (5) ◽  
pp. 914-930 ◽  
Author(s):  
Ryan L. Fogt ◽  
David H. Bromwich
Keyword(s):  
Liquid Water ◽  
Cloud Cover ◽  
Supercooled Liquid ◽  
South Pole ◽  
Atmospheric Moisture ◽  
Microphysics Scheme ◽  
Low Level ◽  
High Level ◽  
Cloud Ice ◽  
Moisture Variability

Abstract Antarctic Mesoscale Prediction System (AMPS) forecasts of atmospheric moisture and cloud fraction (CF) are compared with observations at McMurdo and Amundsen–Scott South Pole station (hereafter, South Pole station) in Antarctica. Overall, it is found that the model produces excessive moisture at both sites in the mid- to upper troposphere because of a weaker vertical decrease of moisture in AMPS than observed. Correlations with observations suggest AMPS does a reasonable job of capturing the low-level moisture variability at McMurdo and the upper-level moisture variability at South Pole station. The model underpredicts the cloud cover at both locations, but changes to the AMPS empirical CF algorithm remove this negative bias by more than doubling the weight given to the cloud ice path. A “pseudosatellite” product based on the microphysical quantities of cloud ice and cloud liquid water within AMPS is preliminarily evaluated against Defense Meteorological Satellite Program (DMSP) imagery during summer to examine the broader performance of cloud variability in AMPS. These comparisons reveal that the model predicts high-level cloud cover and movement with fidelity, which explains the good agreement between the modified CF algorithm and the observed CF. However, this product also demonstrates deficiencies in capturing low-level cloudiness over cold ice surfaces primarily related to insufficient supercooled liquid water produced by the microphysics scheme, which also reduces the CF correlation with observations. The results suggest that AMPS predicts the overall CF amount and high cloud variability notably well, making it a reliable tool for longer-term climate studies of these fields in Antarctica.

scholarly journals Trends in spectrally resolved outgoing longwave radiation from 10 years of satellite measurements

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Simon Whitburn ◽  
Lieven Clarisse ◽  
Marie Bouillon ◽  
Sarah Safieddine ◽  
Maya George ◽  
...  
Keyword(s):  
Outgoing Longwave Radiation ◽  
Southern Oscillation ◽  
Longwave Radiation ◽  
Atmospheric Temperature ◽  
Natural Variability ◽  
Satellite Measurements ◽  
Clear Sky ◽  
The Individual ◽  
Spectrally Resolved

AbstractIn recent years, the interest has grown in satellite-derived hyperspectral radiance measurements for assessing the individual impact of climate drivers and their cascade of feedbacks on the outgoing longwave radiation (OLR). In this paper, we use 10 years (2008–2017) of reprocessed radiances from the infrared atmospheric sounding interferometer (IASI) to evaluate the linear trends in clear-sky spectrally resolved OLR (SOLR) in the range [645–2300] cm−1. Spatial inhomogeneities are observed in most of the analyzed spectral regions. These mostly reflected the natural variability of the atmospheric temperature and composition but long-term changes in greenhouse gases concentrations are also highlighted. In particular, the increase of atmospheric CO2 and CH4 led to significant negative trends in the SOLR of −0.05 to −0.3% per year in the spectral region corresponding to the ν2 and the ν3 CO2 and in the ν4 CH4 band. Most of the trends associated with the natural variability of the OLR can be related to the El Niño/Southern Oscillation activity and its teleconnections in the studied period. This is the case for the channels most affected by the temperature variations of the surface and the first layers of the atmosphere but also for the channels corresponding to the ν2 H2O and the ν3 O3 bands.

scholarly journals SegCloud: a novel cloud image segmentation model using deep Convolutional Neural Network for ground-based all-sky-view camera observation

2019 ◽  
Author(s):  
Wanyi Xie ◽  
Dong Liu ◽  
Ming Yang ◽  
Shaoqing Chen ◽  
Benge Wang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Cloud Cover ◽  
Weather Forecast ◽  
Cloud Detection ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Softmax Classifier ◽  
Discrimination Ability ◽  
Low Level ◽  
High Level

Abstract. Cloud detection and cloud properties have significant applications in weather forecast, signal attenuation analysis, and other cloud-related fields. Cloud image segmentation is the fundamental and important step to derive cloud cover. However, traditional segmentation methods rely on low-level visual features of clouds, and often fail to achieve satisfactory performance. Deep Convolutional Neural Networks (CNNs) are able to extract high-level feature information of object and have become the dominant methods in many image segmentation fields. Inspired by that, a novel deep CNN model named SegCloud is proposed and applied to accurate cloud segmentation based on ground-based observation. Architecturally, SegCloud possesses symmetric encoder-decoder structure. The encoder network combines low-level cloud features to form high-level cloud feature maps with low resolution, and the decoder network restores the obtained high-level cloud feature maps to the same resolution of input images. The softmax classifier finally achieves pixel-wise classification and outputs segmentation results. SegCloud has powerful cloud discrimination ability and can automatically segment the whole sky images obtained by a ground-based all-sky-view camera. Furthermore, a new database, which includes 400 whole sky images and manual-marked labels, is built to train and test the SegCloud model. The performance of SegCloud is validated by extensive experiments, which show that SegCloud is effective and accurate for ground-based cloud segmentation and achieves better results than traditional methods. Moreover, the accuracy and practicability of SegCloud is further proved by applying it to cloud cover estimation.

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