An assessment of differences in lower stratospheric temperature records from (A)MSU, radiosondes, and GPS radio occultation

Abstract. Uncertainties for upper-air trend patterns are still substantial. Observations from the radio occultation (RO) technique offer new opportunities to assess the existing observational records there. Long-term time series are available from radiosondes and from the (Advanced) Microwave Sounding Unit (A)MSU. None of them were originally intended to deliver data for climate applications. Demanding intercalibration and homogenization procedures are required to account for changes in instrumentation and observation techniques. In this comparative study three (A)MSU anomaly time series and two homogenized radiosonde records are compared to RO data from the CHAMP, SAC-C, GRACE-A and F3C missions for September 2001 to December 2009. Differences of monthly anomalies are examined to assess the differences in the datasets due to structural uncertainties. The difference of anomalies of the (A)MSU datasets relative to RO shows a statistically significant trend of about (−0.2 ± 0.05) K at all latitudes. This signals a divergence of the two datasets over time. The radiosonde network has known deficiencies in its global coverage, with sparse representation of most of the Southern Hemisphere, the tropics and the oceans. In this study the error that results from sparse sampling is estimated and accounted for by subtracting it from radiosonde and RO datasets. Surprisingly the sampling error correction is also important in the Northern Hemisphere (NH), where the radiosonde network is dense over the continents but does not capture large atmospheric variations in NH winter. Considering the sampling error, the consistency of radiosonde and RO anomalies is improving substantially; there is no significant trend in the anomaly differences at global scale and in the NH. Regarding (A)MSU, its poor vertical resolution poses another problem by missing important features of the vertical atmospheric structure. This demonstrates the advantage of homogeneously distributed measurements with high vertical resolution.

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An assessment of differences in lower stratospheric temperature records from (A)MSU, radiosondes, and GPS radio occultation

Atmospheric Measurement Techniques ◽

10.5194/amt-4-1965-2011 ◽

2011 ◽

Vol 4 (9) ◽

pp. 1965-1977 ◽

Cited By ~ 21

Author(s):

F. Ladstädter ◽

A. K. Steiner ◽

U. Foelsche ◽

L. Haimberger ◽

C. Tavolato ◽

...

Keyword(s):

Time Series ◽

Radio Occultation ◽

Sampling Error ◽

Vertical Resolution ◽

Stratospheric Temperature ◽

Microwave Sounding ◽

The Difference ◽

Temperature Records ◽

Atmospheric Variations ◽

The Tropics

Abstract. Uncertainties for upper-air trend patterns are still substantial. Observations from the radio occultation (RO) technique offer new opportunities to assess the existing observational records there. Long-term time series are available from radiosondes and from the (Advanced) Microwave Sounding Unit (A)MSU. None of them were originally intended to deliver data for climate applications. Demanding intercalibration and homogenization procedures are required to account for changes in instrumentation and observation techniques. In this comparative study three (A)MSU anomaly time series and two homogenized radiosonde records are compared to RO data from the CHAMP, SAC-C, GRACE-A and F3C missions for September 2001 to December 2010. Differences of monthly anomalies are examined to assess the differences in the datasets due to structural uncertainties. The difference of anomalies of the (A)MSU datasets relative to RO shows a statistically significant trend within about (−0.2±0.1) K/10 yr (95% confidence interval) at all latitudes. This signals a systematic deviation of the two datasets over time. The radiosonde network has known deficiencies in its global coverage, with sparse representation of most of the southern hemisphere, the tropics and the oceans. In this study the error that results from sparse sampling is estimated and accounted for by subtracting it from radiosonde and RO datasets. Surprisingly the sampling error correction is also important in the Northern Hemisphere (NH), where the radiosonde network is dense over the continents but does not capture large atmospheric variations in NH winter. Considering the sampling error, the consistency of radiosonde and RO anomalies is improving substantially; the trend in the anomaly differences is generally very small. Regarding (A)MSU, its poor vertical resolution poses another problem by missing important features of the vertical atmospheric structure. This points to the advantage of homogeneously distributed measurements with high vertical resolution.

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Urban heat island estimation from improved selection of urban and rural stations by DTW algorithm

10.21203/rs.3.rs-164196/v1 ◽

2021 ◽

Author(s):

Yonghong Hu ◽

Gensuo Jia ◽

Jinlong Ai ◽

Yong Zhang ◽

Meiting Hou ◽

...

Keyword(s):

Urban Heat Island ◽

Urban Climate ◽

Global Scale ◽

Heat Island ◽

Climate Effect ◽

Urban Heat ◽

The Difference ◽

Temperature Records ◽

Dynamic Time ◽

Selection Of

Abstract Typical urban and rural temperature records are essential for the estimation and comparison of urban heat island effects in different regions, and the key issues are how to identify the typical urban and rural stations. This study tried to analyze the similarity of air temperature sequences by using dynamic time warping algorithm (DTW) to improve the selection of typical stations. We examined the similarity of temperature sequences of 20 stations in Beijing and validated by remote sensing, and the results indicated that DTW algorithm could identify the difference of temperature sequence, and clearly divide them into different groups according to their probability distribution information. The analysis for station pairs with high similarity could provide appropriate classification for typical urban stations (FT, SY, HD, TZ, CY, CP, MTG, BJ, SJS, DX, FS) and typical rural stations (ZT, SDZ, XYL) in Beijing. We also found that some traditional rural stations can’t represent temperature variation in rural surface because of their surrounding environments highly modified by urbanization process in last decades, and they may underestimate the urban climate effect by 1.24℃. DTW algorithm is simple in analysis and application for temperature sequences, and has good potentials in improving urban heat island estimation in regional or global scale by selecting more appropriate temperature records.

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Over-whitening or Pre-whitening in Hydro-climatological Trend Analysis?

10.21203/rs.3.rs-1183199/v1 ◽

2022 ◽

Author(s):

Zekai Sen

Keyword(s):

Time Series ◽

White Noise ◽

Trend Analysis ◽

Serial Correlation ◽

Original Time Series ◽

Trend Component ◽

The Difference ◽

Temperature Records ◽

Original Time ◽

Trend Identification

Abstract To meet the basic assumption of classical Mann-Kendall (MK) trend analysis, which requires serially independent time series, a pre-whitening (PW) procedure is proposed to alleviate the serial correlation structure of a given hydro-meteorological time series records for application. The procedure is simply to take the lagged differences in a given time series in the hope that the new time series will have an independent serial correlation coefficient. The whole idea was originally based on the first-order autoregressive AR (1) process, but such a procedure has been documented to damage the trend component in the original time series. On the other hand, the over-whitening procedure (OW) proposes a white noise process superposition of the same length with zero mean and some standard deviation on the original time series to convert it into serially independent series without any damage to the trend component. The stationary white noise addition does not have any trend components. For trend identification, annual average temperature records in New Jersey and Istanbul are presented to show the difference between PW and OW procedures. It turned out that the OW procedure was superior to the PW procedure, which did not cause a loss in the original trend component.

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Refractivity and temperature climate records from multiple radio occultation satellites consistent within 0.05%

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-4-1593-2011 ◽

2011 ◽

Vol 4 (2) ◽

pp. 1593-1615 ◽

Cited By ~ 9

Author(s):

U. Foelsche ◽

B. Scherllin-Pirscher ◽

F. Ladstädter ◽

A. K. Steiner ◽

G. Kirchengast

Keyword(s):

Radio Occultation ◽

Sampling Error ◽

Temporal Trends ◽

Data Consistency ◽

Satellite Mission ◽

Processing Scheme ◽

Climate Record ◽

Abstract Data ◽

Temperature Records ◽

Temperature Climate

Abstract. Data consistency is an important prerequisite to build radio occultation (RO) climatologies based on a combined record of data from different satellites. The presence of multiple RO receiving satellites in orbit over the same time period allows for testing this consistency. We used RO data from CHAMP (CHAllenging Minisatellite Payload for geoscientific research), six FORMOSAT-3/COSMIC satellites (Formosa Satellite Mission 3/Constellation Observing System for Meteorology, Ionosphere and Climate, F3C), and GRACE-A (Gravity Recovery and Climate Experiment). We show latitude-altitude-resolved results for an example month (October 2007) and the temporal evolution of differences in a climate record of global and monthly means from January 2007 to December 2009. Latitude- and altitude-resolved refractivity and dry temperature climatologies clearly show the influence of different sampling characteristics; monthly mean deviations from the multi-satellite mean over the altitude domain 10 km to 30 km typically reach 0.1% and 0.2 K, respectively. Nevertheless, the 3-year average deviations (shorter for CHAMP) are less than 0.03% and 0.05 K, respectively. We find no indications for instrument degradation, instationarities in the RO records, or temporal trends in sampling patterns. Based on analysis fields from ECMWF (European Centre for Medium-Range Weather Forecasts), we can estimate – and subtract – the sampling error from each monthly climatology. After such subtraction, refractivity deviations are found reduced to <0.05% in almost any month and dry temperature deviations to <0.05 K (<0.02% relative) for almost every satellite and month. 3-year average deviations are even reduced to <0.01% and <0.01 K (CHAMP: −0.05 K), respectively, establishing an amazing consistency of RO climatologies from different satellites. If applying the same processing scheme for all data, refractivity and dry temperature records from individual satellites with similar bending angle noise can be safely combined up to 30 km altitude (refractivity also up to 35 km) to a consistent single climate record of substantial value for climate monitoring in the upper troposphere and lower stratosphere.

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Refractivity and temperature climate records from multiple radio occultation satellites consistent within 0.05%

Atmospheric Measurement Techniques ◽

10.5194/amt-4-2007-2011 ◽

2011 ◽

Vol 4 (9) ◽

pp. 2007-2018 ◽

Cited By ~ 46

Author(s):

U. Foelsche ◽

B. Scherllin-Pirscher ◽

F. Ladstädter ◽

A. K. Steiner ◽

G. Kirchengast

Keyword(s):

Radio Occultation ◽

Sampling Error ◽

Temporal Trends ◽

Data Consistency ◽

Satellite Mission ◽

Processing Scheme ◽

Climate Record ◽

Abstract Data ◽

Temperature Records ◽

Temperature Climate

Abstract. Data consistency is an important prerequisite to build radio occultation (RO) climatologies based on a combined record of data from different satellites. The presence of multiple RO receiving satellites in orbit over the same time period allows for testing this consistency. We used RO data from CHAMP (CHAllenging Minisatellite Payload for geoscientific research), six FORMOSAT-3/COSMIC satellites (Formosa Satellite Mission 3/Constellation Observing System for Meteorology, Ionosphere and Climate, F3C), and GRACE-A (Gravity Recovery and Climate Experiment). We show latitude-altitude-resolved results for an example month (October 2007) and the temporal evolution of differences in a climate record of global and monthly means from January 2007 to December 2009. Latitude- and altitude-resolved refractivity and dry temperature climatologies clearly show the influence of different sampling characteristics; monthly mean deviations from the multi-satellite mean over the altitude domain 10 km to 30 km typically reach 0.1% and 0.2 K, respectively. Nevertheless, the 3-yr average deviations (shorter for CHAMP) are less than 0.03% and 0.05 K, respectively. We find no indications for instrument degradation, temporal inhomogeneities in the RO records, or temporal trends in sampling patterns. Based on analysis fields from ECMWF (European Centre for Medium-Range Weather Forecasts), we can estimate – and subtract – the sampling error from each monthly climatology. After such subtraction, refractivity deviations are found reduced to <0.05% in almost any month and dry temperature deviations to <0.05 K (<0.02% relative) for almost every satellite and month. 3-yr average deviations are even reduced to <0.01% and <0.01 K (CHAMP: −0.05 K), respectively, establishing an amazing consistency of RO climatologies from different satellites. If applying the same processing scheme for all data, refractivity and dry temperature records from individual satellites with similar bending angle noise can be safely combined up to 30 km altitude (refractivity also up to 35 km) to a consistent single climate record of substantial value for climate monitoring in the upper troposphere and lower stratosphere.

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Pressure and Temperature Differences between Vaisala RS80 and RS92 Radiosonde Systems

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2007jtecha999.1 ◽

2008 ◽

Vol 25 (6) ◽

pp. 909-927 ◽

Cited By ~ 48

Author(s):

Wolfgang Steinbrecht ◽

Hans Claude ◽

Fritz Schönenborn ◽

Ulrich Leiterer ◽

Horst Dier ◽

...

Keyword(s):

Global Positioning System ◽

Stratospheric Temperature ◽

Global Positioning ◽

System Data ◽

The Mean ◽

The Difference ◽

Temperature Records ◽

Nighttime Temperatures ◽

Climate Studies ◽

Radiation Error

Abstract In several twin flight campaigns, Vaisala RS80 radiosonde systems report lower temperatures than Vaisala RS92 systems in the daytime. Simultaneous differences increase from less than 0.1 K at pressure altitudes below 100 hPa to 0.7 K at 10 hPa. Much of the difference can be explained by an overcorrection of the RS80 radiation error. At night, RS92 and RS80 sounding systems report very similar simultaneous temperatures throughout the atmosphere. Geopotential heights from RS92 pressure, temperature, and humidity data (pTU heights) are within 25 m of geopotential heights from the RS92 global positioning system data (GPS heights) from the ground up to about 70 hPa. At higher altitudes, RS92 sondes produced after July 2004 show nearly identical pTU and GPS heights, but other manufacturing batches show systematic differences, up to ±100 m near 10 hPa. RS80 sondes provide much less accurate pressure and geopotential height. On average, they give up to 1 hPa higher pressure and 20 m lower pTU heights than RS92 sondes in the troposphere, and lower pressures and larger heights in the stratosphere (e.g., by −0.4 hPa and +100 m near 10 hPa). Previous intercomparisons have found similar differences between the two sonde types. As expected from these simultaneous pressure and temperature differences, the transition from Vaisala RS80 to RS92 sondes at German radiosonde stations between 2003 and 2005 has produced artificial increases in stratospheric temperature records, particularly during daytime (1200 UTC), but due to the wrong RS80 pressures, also at night (0000 UTC). The spurious daytime temperature step reaches +0.3 ± 0.2 K at 50 hPa and +0.7 ± 0.4 K at 10 hPa, the nighttime step reaches +0.1 ± 0.1 K at 50 hPa and +0.35 ± 0.2 K at 10 hPa (2σ uncertainties). The mean difference between day- and nighttime temperatures (1200–0000 UTC) has increased as well, by 0.1 ± 0.06 K at 70 hPa and by 0.76 ± 0.16 K at 10 hPa. In the troposphere and at levels below 100 hPa no significant differences are found, although there are indications for higher daytime temperatures, possibly up to 0.1 K, from RS92 sondes. Results indicate that RS92 sondes are more accurate. Historic temperature records from RS80 sondes should be corrected for use in climate studies.

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Homogenized Time Series of the Atmospheric Water Vapor Content Obtained from the GNSS Reprocessed Data

Journal of Climate ◽

10.1175/jcli-d-15-0158.1 ◽

2016 ◽

Vol 29 (7) ◽

pp. 2443-2456 ◽

Cited By ~ 21

Author(s):

T. Ning ◽

J. Wickert ◽

Z. Deng ◽

S. Heise ◽

G. Dick ◽

...

Keyword(s):

Time Series ◽

Water Vapor ◽

Global Scale ◽

Global Navigation Satellite Systems ◽

Radiosonde Data ◽

Gps Data ◽

Satellite Systems ◽

Weather Forecasts ◽

The Difference ◽

Global Navigation Satellite

Abstract The potential temporal shifts in the integrated water vapor (IWV) time series obtained from reprocessed data acquired from global navigation satellite systems (GNSS) were comprehensively investigated. A statistical test, the penalized maximal t test modified to account for first-order autoregressive noise in time series (PMTred), was used to identify the possible mean shifts (changepoints) in the time series of the difference between the GPS IWV and the IWV obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) data. The approach allows for identification of the changepoints not only in the GPS IWV time series but also in ERA-Interim. The IWV difference time series formed for 101 GPS sites were tested, where 47 of them were found to contain in total 62 changepoints. The results indicate that 45 detected changepoints were due to the inconsistencies in the GPS IWV time series, and 16 were related to ERA-Interim, while one point was left unverified. After the correction of the mean shifts for the GPS data, an improved consistency in the IWV trends is evident between nearby sites, while a better agreement is seen between the trends from the GPS and ERA-Interim data on a global scale. In addition, the IWV trends estimated for 47 GPS sites were compared to the corresponding IWV trends obtained from nearby homogenized radiosonde data. The correlation coefficient of the trends increases significantly by 38% after using the homogenized GPS data.

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EVALUASI PROYEKSI DEMAND BARANG DAN PENUMPANG PELABUHAN DI PROVINSI MALUKU

Jurnal Transportasi ◽

10.26593/jt.v19i2.3467.101-110 ◽

2019 ◽

Vol 19 (2) ◽

pp. 101-110

Author(s):

Adrian Firdaus ◽

M. Dwi Yoga Sutanto ◽

Rajin Sihombing ◽

M. Weldy Hermawan

Keyword(s):

Time Series ◽

Projection Method ◽

Development Plan ◽

Master Plan ◽

Port Development ◽

Loading And Unloading ◽

The Difference ◽

Existing Data

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

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279 WACSAW: A Statistical and Individually Adaptive Method to Determine Sleep and Wakefulness from Activity

SLEEP ◽

10.1093/sleep/zsab072.278 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A111-A112

Author(s):

Austin Vandegriffe ◽

V A Samaranayake ◽

Matthew Thimgan

Keyword(s):

Time Series ◽

Optimal Transport ◽

Activity Patterns ◽

Adaptive Method ◽

Wasserstein Distance ◽

Activity Data ◽

Commercial Grade ◽

Movement Data ◽

The Difference ◽

Levene Test

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):

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Multiscale Mutual Mode Entropy Based Coupling Analysis of Alpha Rhythm Electroencephalogram

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.574.718 ◽

2014 ◽

Vol 574 ◽

pp. 718-722

Author(s):

Ning Ji ◽

Jun Tan ◽

An Shan Pei ◽

Jia Fei Dai ◽

Jun Wang

Keyword(s):

Time Series ◽

Clinical Evaluation ◽

Brain Function ◽

Alpha Rhythm ◽

Physiological Status ◽

Noise Resistance ◽

Middle Aged ◽

Coupling Analysis ◽

Scale Change ◽

The Difference

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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