scholarly journals Evaluating the Absolute Calibration Accuracy and Stability of AIRS Using the CMC SST

2020 ◽  
Vol 12 (17) ◽  
pp. 2743
Author(s):  
Hartmut H. Aumann ◽  
Steven E. Broberg ◽  
Evan M. Manning ◽  
Thomas S. Pagano ◽  
Robert C. Wilson

We compare the daily mean and standard deviation of the difference between the sea surface skin temperature (SST) derived from clear sky Atmospheric InfraRed Sounder (AIRS) data from seven atmospheric window channels between 2002 and 2020 and collocated Canadian Meteorological Centre (CMC) SST data from the tropical oceans. After correcting the mean difference for cloud contamination and diurnal effects, the remaining bias relative to the CMC SST, is reasonably consistent with estimates of the AIRS absolute accuracy based on the uncertainty of the pre-launch calibration. The time series of the bias produces trends well below the 10 mK/yr level required for climate change evaluations. The trends are in the 2 mK/yr range for the five window channels between 790 and 1231 cm−1, and +5 mK/yr for the shortwave channels. Between 2002 and 2020, the time series of the standard deviation of the difference between the AIRS SST and the CMC SST dropped fairly steadily to below 0.4 K in several AIRS window channels, a level previously only seen in gridded SST products relative to the Argo buoys.

2012 ◽  
Vol 29 (3) ◽  
pp. 375-381 ◽  
Author(s):  
Xianglei Huang ◽  
Norman G. Loeb ◽  
Huiwen Chuang

Abstract Clouds and the Earth’s Radiant Energy System (CERES) daytime longwave (LW) radiances are determined from the difference between a total (TOT) channel (0.3–200 μm) measurement and a shortwave (SW) channel (0.3–5 μm) measurement, while nighttime LW radiances are obtained directly from the TOT channel. This means that a drift in the SW channel or the SW portion of the TOT channel could impact the daytime longwave radiances, but not the nighttime ones. This study evaluates daytime and nighttime CERES LW radiances for a possible secular drift in CERES LW observations using spectral radiances observed by Atmospheric Infrared Sounder (AIRS). By examining the coincidental AIRS and CERES Flight Model 3 (FM3) measurements over the tropical clear-sky oceans for all of January and July months since 2005, a secular drift of about −0.11% yr−1 in the daytime CERES-FM3 longwave unfiltered radiance can be identified in the CERES Single Scanner Footprint (SSF) Edition 2 product. This provides an upper-bound estimation for the drift in daytime outgoing longwave radiation, which is approximately −0.323 W m−2 yr−1. This estimation is consistent with the independent assessment concluded by the CERES calibration team. Such secular drift has been greatly reduced in the latest CERES SSF Edition 3 product. Comparisons are conducted for the CERES window channel as well, and it shows essentially no drift. This study serves as a practical example illustrating how the measurements of spectrally resolved radiances can be used to help evaluate data products from other narrowband or broadband measurements.


2010 ◽  
Vol 27 (3) ◽  
pp. 470-480 ◽  
Author(s):  
Chee-Kiat Teo ◽  
Tieh-Yong Koh

Abstract A statistical method to correct for the limb effect in off-nadir Atmospheric Infrared Sounder (AIRS) channel radiances is described, using the channel radiance itself and principal components (PCs) of the other channel radiances to account for the multicollinearity. A method of selecting an optimal set of predictors is proposed and demonstrated for one- and two-PC predictors. Validation results with a subset of AIRS channels in the spectral region 649–2664 cm−1 show that the mean nadir-corrected brightness temperature (BT) is largely independent of scan angle. More than 66% of the channels have a root-mean-square (rms) bias less than 0.10 K after nadir correction. Limb effect on the standard deviation (SD) of BT is discernible at larger scan angles, mainly for the atmospheric windows and the water vapor channels around 6.7 μm. After nadir correction, nearly all atmospheric window channels unaffected by solar glint and more than 76% of water vapor channels examined have BT SDs brought closer to nadir values. For the window channels affected by solar glint (wavenumber > 2490 cm−1), BT SDs at the scan angles with the strongest impact from solar reflection were improved on average by more than 0.6 K after nadir correction.


2014 ◽  
Vol 27 (12) ◽  
pp. 4403-4420 ◽  
Author(s):  
Seiji Kato ◽  
Fred G. Rose ◽  
Xu Liu ◽  
Bruce A. Wielicki ◽  
Martin G. Mlynczak

Abstract A surface, atmospheric, and cloud (fraction, height, optical thickness, and particle size) property anomaly retrieval from highly averaged longwave spectral radiances is simulated using 28 years of reanalysis. Instantaneous nadir-view spectral radiances observed from an instrument on a 90° inclination polar orbit are computed. Spectral radiance changes caused by surface, atmospheric, and cloud property perturbations are also computed and used for the retrieval. This study’s objectives are 1) to investigate whether or not separating clear sky from cloudy sky reduces the retrieval error and 2) to estimate the error in a trend of retrieved properties. This simulation differs from earlier studies in that annual 10° latitude zonal cloud and atmospheric property anomalies defined as the deviation from 28-yr climatological means are retrieved instead of the difference of these properties from two time periods. The root-mean-square (RMS) difference of temperature and humidity anomalies retrieved from all-sky radiance anomalies is similar to the RMS difference derived from clear-sky radiance anomalies computed by removing clouds. This indicates that the cloud property anomaly retrieval error does not affect the retrieved temperature and humidity anomalies. When retrieval errors are nearly random, the error in the trend of retrieved properties is small. Approximately 30% of 10° latitude zones meet conditions that the true temperature and water vapor amount trends are within a 95% confidence interval of retrieved trends, and that the standard deviation of retrieved anomalies σret is within 20% of the standard deviation of true anomalies σn. If σret/σn − 1 is within ±0.2, 91% of the true trends fall within the 95% confidence interval of the corresponding retrieved trend.


2020 ◽  
Vol 12 (2) ◽  
pp. 340 ◽  
Author(s):  
Dongxiang Wang ◽  
Iwona S. Stachlewska ◽  
Xiaoquan Song ◽  
Birgit Heese ◽  
Anca Nemuc

Atmospheric boundary layer height (ABLH) was observed by the CHM15k ceilometer (January 2008 to October 2013) and the PollyXT lidar (July 2013 to December 2018) over the European Aerosol Research LIdar NETwork to Establish an Aerosol Climatology (EARLINET) site at the Remote Sensing Laboratory (RS-Lab) in Warsaw, Poland. Out of a maximum number of 4017 observational days within this period, a subset of quasi-continuous measurements conducted with these instruments at the same wavelength (1064 nm) was carefully chosen. This provided a data sample of 1841 diurnal cycle ABLH observations. The ABLHs were derived from ceilometer and lidar signals using the wavelet covariance transform method (WCT), gradient method (GDT), and standard deviation method (STD). For comparisons, the rawinsondes of the World Meteorological Organization (WMO 12374 site in Legionowo, 25 km distance to the RS-Lab) were used. The ABLHs derived from rawinsondes by the skew-T-log-p method and the bulk Richardson (bulk-Ri) method had a linear correlation coefficient (R2) of 0.9 and standard deviation (SD) of 0.32 km. A comparison of the ABLHs obtained for different methods and instruments indicated a relatively good agreement. The ABLHs estimated from the rawinsondes with the bulk-Ri method had the highest correlations, R2 of 0.80 and 0.70 with the ABLHs determined using the WCT method on ceilometer and lidar signals, respectively. The three methods applied to the simultaneous, collocated lidar, and ceilometer observations (July to October 2013) showed good agreement, especially for the WCT method (R2 of 0.94, SD of 0.19 km). A scaling threshold-based algorithm was proposed to homogenize ceilometer and lidar datasets, which were applied on the lidar data, and significantly improved the coherence of the results (R2 of 0.98, SD of 0.11 km). The difference of ABLH between clear-sky and cloudy conditions was on average below 230 m for the ceilometer and below 70 m for the lidar retrievals. The statistical analysis of the long-term observations indicated that the monthly mean ABLHs varied throughout the year between 0.6 and 1.8 km. The seasonal mean ABLH was of 1.16 ± 0.16 km in spring, 1.34 ± 0.15 km in summer, 0.99 ± 0.11 km in autumn, and 0.73 ± 0.08 km in winter. In spring and summer, the daytime and nighttime ABLHs appeared mainly in a frequency distribution range of 0.6 to 1.0 km. In winter, the distribution was common between 0.2 and 0.6 km. In autumn, it was relatively balanced between 0.2 and 1.2 km. The annual mean ABLHs maintained between 0.77 and 1.16 km, whereby the mean heights of the well-mixed, residual, and nocturnal layer were 1.14 ± 0.11, 1.27 ± 0.09, and 0.71 ± 0.06 km, respectively (for clear-sky conditions). For the whole observation period, the ABLHs below 1 km constituted more than 60% of the retrievals. A strong seasonal change of the monthly mean ABLH diurnal cycle was evident; a mild weakly defined autumn diurnal cycle, followed by a somewhat flat winter diurnal cycle, then a sharp transition to a spring diurnal cycle, and a high bell-like summer diurnal cycle. A prolonged summertime was manifested by the September cycle being more similar to the summer than autumn cycles.


2020 ◽  
Author(s):  
Hartmut Aumann ◽  
Evan Manning ◽  
Chris Wilson ◽  
Jorge Vasquez

<p>The Sea Surface Temperature (SST) is a key component of climate research and daily globally gridded SST products are a key input to this effort.  Here we evaluate the NOAA RTGSST, which goes back to 1996, the Canada Meteorological Center (CMC) SST, available since 2002, and the OSTIA SST by the UK MetOffice, available since 2012. The calibration of the three products is tied to the moored and floating buoys along the equator, but there are differences in the way all grid points are optimally filled. The 2016 annual mean between 30S and 30N, 299.7K, differed by only 8 mK. However zonal mean differences between the three products north of 30N and south of 30S latitude are  of the order of 150 mK, and of opposite signs. Even more puzzling is that during 2016 the CMC was on average 150 mK colder than the OSTIA at 280K, while being warmer by 150mK at 290K. Differences of this magnitude are of concern when measure warming of the oceans at the rate of 15 mK/year. We use the daily mean and standard deviation and trends of the difference between the SST measured with AIRS (Atmospheric Infrared Sounder) since 2002 and CrIS (Crosstrack Interferometer Sounder) since 2012 to evaluate the three products.         </p>


2019 ◽  
Vol 19 (2) ◽  
pp. 101-110
Author(s):  
Adrian Firdaus ◽  
M. Dwi Yoga Sutanto ◽  
Rajin Sihombing ◽  
M. Weldy Hermawan

Abstract Every port in Indonesia must have a Port Master Plan that contains an integrated port development plan. This study discusses one important aspect in the preparation of the Port Master Plan, namely the projected movement of goods and passengers, which can be used as a reference in determining the need for facilities at each stage of port development. The case study was conducted at a port located in a district in Maluku Province and aims to evaluate the analysis of projected demand for goods and passengers occurring at the port. The projection method used is time series and econometric projection. The projection results are then compared with the existing data in 2018. The results of this study show that the econometric projection gives adequate results in predicting loading and unloading activities as well as the number of passenger arrival and departure in 2018. This is indicated by the difference in the percentage of projection results towards the existing data, which is smaller than 10%. Whereas for loading and unloading activities, time series projections with logarithmic trends give better results than econometric projections. Keywords: port, port master plan, port development, unloading activities  Abstrak Setiap pelabuhan di Indonesia harus memiliki sebuah Rencana Induk Pelabuhan yang memuat rencana pengem-bangan pelabuhan secara terpadu. Studi ini membahas salah satu aspek penting dalam penyusunan Rencana Induk Pelabuhan, yaitu proyeksi pergerakan barang dan penumpang, yang dapat dipakai sebagai acuan dalam penentuan kebutuhan fasilitas di setiap tahap pengembangan pelabuhan. Studi kasus dilakukan pada sebuah pelabuhan yang terletak di sebuah kabupaten di Provinsi Maluku dan bertujuan untuk melakukan evaluasi ter-hadap analisis proyeksi demand barang dan penumpang yang terjadi di pelabuhan tersebut. Metode proyeksi yang dipakai adalah proyeksi deret waktu dan ekonometrik. Hasil proyeksi selanjutnya dibandingkan dengan data eksisting tahun 2018. Hasil studi ini menunjukkan bahwa proyeksi ekonometrik memberikan hasil yang cukup baik dalam memprediksi aktivitas bongkar barang serta jumlah penumpang naik dan turun di tahun 2018. Hal ini diindikasikan dengan selisih persentase hasil proyeksi terhadap data eksisting yang lebih kecil dari 10%. Sedangkan untuk aktivitas muat barang, proyeksi deret waktu dengan tren logaritmik memberikan hasil yang lebih baik daripada proyeksi ekonometrik. Kata-kata kunci: pelabuhan, rencana induk pelabuhan, pengembangan pelauhan, aktivitas bongkar barang


SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A111-A112
Author(s):  
Austin Vandegriffe ◽  
V A Samaranayake ◽  
Matthew Thimgan

Abstract Introduction Technological innovations have broadened the type and amount of activity data that can be captured in the home and under normal living conditions. Yet, converting naturalistic activity patterns into sleep and wakefulness states has remained a challenge. Despite the successes of current algorithms, they do not fill all actigraphy needs. We have developed a novel statistical approach to determine sleep and wakefulness times, called the Wasserstein Algorithm for Classifying Sleep and Wakefulness (WACSAW), and validated the algorithm in a small cohort of healthy participants. Methods WACSAW functional routines: 1) Conversion of the triaxial movement data into a univariate time series; 2) Construction of a Wasserstein weighted sum (WSS) time series by measuring the Wasserstein distance between equidistant distributions of movement data before and after the time-point of interest; 3) Segmenting the time series by identifying changepoints based on the behavior of the WSS series; 4) Merging segments deemed similar by the Levene test; 5) Comparing segments by optimal transport methodology to determine the difference from a flat, invariant distribution at zero. The resulting histogram can be used to determine sleep and wakefulness parameters around a threshold determined for each individual based on histogram properties. To validate the algorithm, participants wore the GENEActiv and a commercial grade actigraphy watch for 48 hours. The accuracy of WACSAW was compared to a detailed activity log and benchmarked against the results of the output from commercial wrist actigraph. Results WACSAW performed with an average accuracy, sensitivity, and specificity of >95% compared to detailed activity logs in 10 healthy-sleeping individuals of mixed sexes and ages. We then compared WACSAW’s performance against a common wrist-worn, commercial sleep monitor. WACSAW outperformed the commercial grade system in each participant compared to activity logs and the variability between subjects was cut substantially. Conclusion The performance of WACSAW demonstrates good results in a small test cohort. In addition, WACSAW is 1) open-source, 2) individually adaptive, 3) indicates individual reliability, 4) based on the activity data stream, and 5) requires little human intervention. WACSAW is worthy of validating against polysomnography and in patients with sleep disorders to determine its overall effectiveness. Support (if any):


Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 659
Author(s):  
Jue Lu ◽  
Ze Wang

Entropy indicates irregularity or randomness of a dynamic system. Over the decades, entropy calculated at different scales of the system through subsampling or coarse graining has been used as a surrogate measure of system complexity. One popular multi-scale entropy analysis is the multi-scale sample entropy (MSE), which calculates entropy through the sample entropy (SampEn) formula at each time scale. SampEn is defined by the “logarithmic likelihood” that a small section (within a window of a length m) of the data “matches” with other sections will still “match” the others if the section window length increases by one. “Match” is defined by a threshold of r times standard deviation of the entire time series. A problem of current MSE algorithm is that SampEn calculations at different scales are based on the same matching threshold defined by the original time series but data standard deviation actually changes with the subsampling scales. Using a fixed threshold will automatically introduce systematic bias to the calculation results. The purpose of this paper is to mathematically present this systematic bias and to provide methods for correcting it. Our work will help the large MSE user community avoiding introducing the bias to their multi-scale SampEn calculation results.


2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.


2014 ◽  
Vol 574 ◽  
pp. 718-722
Author(s):  
Ning Ji ◽  
Jun Tan ◽  
An Shan Pei ◽  
Jia Fei Dai ◽  
Jun Wang

This paper presents the Multiscale Mutual Mode Entropy algorithm to quantify the coupling degree between two alpha rhythm EEG time series which are simultaneously acquired. The results show that in the process of scale change, the young and middle-aged differ from each other in terms of the coupling degree of alpha rhythm EEG and the difference grow clear gradually. So the Multiscale Mutual Mode Entropy can be used to analyze the coupling information of time series under different physiological status, and it also has good noise resistance. Besides, as an indicator of measuring brain function, in the future it can also come to the aid of clinical evaluation of brain function.


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