AbstractThis paper evaluates the first 15 months of the Ozone Mapping and Profiler Suite (OMPS) Sensor Data Record (SDR) acquired by the nadir sensors and processed by the National Oceanic and Atmospheric Administration Interface Data Processing Segment. The evaluation consists of an inter‐comparison with a similar satellite instrument, an analysis using a radiative transfer model, and an assessment of product stability. This is in addition to the evaluation of sensor calibration and the Environment Data Record product that are also reported in this Special Issue. All these are parts of synergetic effort to provide comprehensive assessment at every level of the products to ensure its quality. It is found that the OMPS nadir SDR quality is satisfactory for the current Provisional maturity. Methods used in the evaluation are being further refined, developed, and expanded, in collaboration with international community through the Global Space‐based Inter‐Calibration System, to support the upcoming long‐term monitoring.

Background Caesarean section at full dilatation can be a technically demanding procedure and has a consistent association with laceration injuries to uterus, cervix and vagina. Recent Scottish Morbidity data showed 25% of women delivered by emergency caesarean section and experiencing massive obstetric haemorrhage (MOH) were delivered in the 2nd stage of labour. 16.3% of all the MOH cases were caused by extensions of the uterine incisions and/or broad ligament haematomas. It is therefore surprising that to date a universally accepted formal classification system for maternal injuries (similar to that of obstetric anal sphincter injuries) relating to this scenario, is yet to emerge. Aim To design a simple classification system and to apply this in a review of second stage deliveries at a UK University hospital Method A retrospective analysis of the labour and operation notes of 60 patients delivered by caesarean section at full dilatation during a 9 month period in 2010. Uterine extensions were graded as: Grade 1 [easy to suture, no increase in operating time], Grade 2 [increased operating time and total blood loss] or Grade 3 [involvement of uterine artery, cervix, vagina, or bladder]. Results 25% [15/60] had uterine extensions of which 53% were Grade1, 27% were grade 2 and 20% were grade 3. It was easy to grade the extensions retrospectively. Grade 3 extensions resulted in longer operating times and higher blood transfusion rates. Conclusion A simple classification of uterine extensions can improve the consistency of contemporaneous documentation and has potential as a research tool.

The current Geostationary Operational Environmental Satellite (GOES) Imager infrared (IR) channels experience a midnight effect that can result in erroneous instrument responsivity around satellite midnight. An empirical method named the Midnight Blackbody Calibration Correction (MBCC) was developed and implemented in the GOES Imager IR operational calibration, aiming to correct the midnight calibration errors. The main objective of this study is to evaluate the MBCC performance for the GOES-11/-12 Imager IR channels by examining the diurnal variation of the mean brightness temperature (Tb) bias with respect to reference instruments. Two well-calibrated hyperspectral radiometers on low Earth orbits (LEOs), the Atmospheric Infrared Sounder on the Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI) on the Metop-A satellite, are used as the reference instruments in this study. However, as the timing of the collocated geostationary-LEO intercalibration data is related to the GOES scan angle, it is then necessary to assess the GOES scan angle calibration variations, which becomes the second objective of this study. Our results show that the applications and performance of the MBCC method varies greatly between the different channels and different times. While it is usually applied with high frequency for about 8 h around satellite midnight for the short-wave channels (Ch2), it may only be intensively used right after satellite midnight or even barely used for the other IR channels. The MBCC method, if applied with high frequency, can reduce the mean day/night calibration difference to less than 0.15 K in almost all the GOES IR channels studied in this paper except for Ch4 (10.7 mu m). The uncertainty of the nighttime GOES and IASI Tb difference for different scan angles is less than 0.1 K in each IR channel, indicating that there is no apparent systematic variation with the scan angle, and therefore, the estimated diurnal cycles of GOES Imager calibration is not prone to the systematic effects due to scan angle.

MetOp-A satellite-based hyper-spectral Infrared Atmospheric Sounding Interferometer IASI observations are used to evaluate the accuracy of the broadband short-wave infrared SWIR atmospheric window channel channel 3B centred at 3.74 μm of the Advanced Very High Resolution Radiometer AVHRR carried on the same platform. To complement the partial spectral coverage of IASI, line-by-line radiative transfer model LBLRTM-simulated IASI spectra are used. The comparisons result in significant negative AVHRR minus IASI bias in radiance ∼–0.04 mW m–2 sr–1 cm–1 with scene temperature dependency in which the absolute value of the bias linearly increases with increasing temperature. It is demonstrated that the negative bias and the scene temperature dependency of the bias are the results of significant absorption in the portion of AVHRR spectral band not seen by IASI, leading to the conclusion that MetOp-A AVHRR channel 3B is not purely an ‘atmospheric window’ channel.

The Geostationary Operational Environmental Satellite (GOES) mission consists of a series of three-axis body-stablized geosynchronous satellites operated by National Oceanic and Atmospheric Admistration (NOAA) that provide continuous stream of data covering the United States and its neighbring environs. Two significant issues associated with the three-axis stablized platforms were recognized in the process of onboard calibration for the infrared data: extraneous heating of the instruments around satellite midnight time, and the changes of scan mirror emissivity with incoming radiation at east-west scan direction. In operation, the Midnight Blackbody Calibration Correction (MBCC) method based on the relationship between day-time calibration data and telemetry temperature was developed to empirically compensate for the errorreous instrument responsivity near midnight. The variation in scan mirror emissivities was corrected with a set of fixed angle-dependent correction coefficients derived from full-disk scan space view data. However, uncertaity remains with MBCC calibration accuracy and the scan angle calibration residuals. The objective of this study is to investigate the impact of the MBCC on the diurnal calibration accuracy of the GOES-11 and -12 Imager infrared (IR) channels, as well as to examine the scan-mirror emissivity calibration residuals using collocation data generated by the Global Space based Inter-Calibration System (GSICS) baseline algorithm. Using two new generation hypespectral instruments onboard low-earth orbital (LEO) satellites as references, the results of this study show that most IR channels, especially the long-wave channels, have apparent diurnal calibration variations. The MBCC method, once implemented, can reduce the diurnal calibration variation except for Ch4(10.7μm) on both GOES-11/12 Imagers. Analysis of the GOES with IASI night-time collocation data (before 10:00pm) indicates that scan-angle calibration residual at each IR channel is very small with uncertainty less than 0.1K. The day-time collocation data may be affected with the anisotropic reflectance/emissivity of the collocated scenes. Our results suggest that either night-time GOES vs. IASI collocation data (taken before the midnight calibration anomaly) or daytime collocated scenes taken nadir/near nadir should be used to generate the GSICS GEO-LEO correction coefficients to improve the calibration accuracy of the operational GOES Imager instruments.

The Advanced Very High Resolution Radiometer (AVHRR) instruments on board NOAA-18, MetOp-A and NOAA-19 satellites are key components of the current operational NOAA-EUMETSAT Initial Joint Polar System (IJPS) and are routinely monitored. Overall, the results of trending analysis show that the AVHRR instruments on NOAA-18/19 and MetOp-A are functioning well outperforming the channel noise specification limits. The backup NOAA-17 AVHRR functioned well for the on-orbit period prior to the onset of scan motor failure around April 11, 2010. The sun-earth-satellite geometry driven seasonality is exhibited by temperature measurements from thermistors on various instrument housing components including blackbody with the exception of patch temperature which is typically maintained stable. The only electrical measurement which exhibits seasonality is patch power. It is shown that the seasonality has no significant adverse impact on AVHRR radiometric performance. On the other hand the space view is adversely affected by intermittent periodic lunar signals and ubiquitous low frequency variability presumably connected to space clamping mechanism. Based on this it is suggested that the AVHRR channel noise estimation should be based on blackbody view. Finally, the temporal stability of the monitored parameters and the smaller or comparable magnitudes of seasonal variability in most of the instrument housekeeping measurements as compared to their orbital variability confirm the good health of AVHRR instruments on-board NOAA-18/19 and MetOp-A.

The collocated measurements in 3.74μm, 11μm, and 12μm channels from Advanced Very High Resolution Radiometer (AVHRR) and corresponding simulated AVHRR measurements using hyper-spectral Infrared Atmospheric Sounding Interferometer (IASI) observations are inter-compared. Both of the instruments are placed on MetOp-A satellite launched in October 2006. Because IASI observations did not have complete spectral coverage over AVHRR 3.74μm channel, Line-By-Line Radiative Transfer Model (LBLRTM) simulated IASI spectra were generated to enable complete IASI coverage for this channel. It is shown that the large AVHRR minus IASI negative bias in 3.74μm channel can be explained more or less completely by the part of the AVHRR spectral band not seen by IASI which is an indication of relatively large absorption in that particular portion of the AVHRR spectral band. The near similarity between slopes of bias dependency on scene radiance from the model and those derived from observations with respect to 3.74μm channel indicate that it could be mostly the CO2 absorption in the higher wave-numbers experienced by AVHRR and not experienced by IASI causing the discrepancy between these two observations. Thus the study confirms that AVHRR short wave infrared channel (3.74 μm) is performing very well with no indication, of spectral uncertainties, or of significant radiometric uncertainties. On the other hand, the results suggest that AVHRR 3.74 um channel experiences significant CO2 absorption which may disqualify it from being recognized as a "window channel." With respect to long wave infrared channels at 11 μm and 12 μm the study reveals that the bias between the two measurements undergo seasonal variations, however, with small magnitudes.

Global Space-based Inter-Calibration System (GSICS) is a critical space component of Global Earth Observation System of Systems (GEOSS) that provided users with high-quality inter-calibrated satellite measurements. As part of the GSICS, imaging instruments on geostationary (GEO) satellites have been inter-calibrated with hyperspectral instrument Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) on Low Earth Orbit (LEO) satellites. This paper reports the GSICS GEO-LEO inter-calibration at NOAA/NESDIS, for GOES-11/12 with AIRS (since January 2007) and IASI (since June 2007), and of METEOSAT-7/8/9, MTSAT-1R, and FY-2C with AIRS and IASI since August 2008. Major components of the operation are reviewed, including algorithm development, data processing, product generation, results dissemination, and selected inter-calibration examples. The preliminary results of the GSICS correction show that the fully functioning GSICS is a powerful tool to monitor instrument performance, to correct sensor bias, and to diagnose the root cause of calibration anomalies.

International Journal of Gynecology & ObstetricsVolume 107, Issue S2 p. S304-S304 Free communication (oral) presentations O737 Reducing complications related to Caesarean section (CS) in second stage: UK experience in the use of fetal disimpacting system (FDS) N. Papanikolaou, N. PapanikolaouSearch for more papers by this authorA. Tillisi, A. TillisiSearch for more papers by this authorL. Louay, L. LouaySearch for more papers by this authorM. Singh, M. SinghSearch for more papers by this authorA. Ikomi, A. IkomiSearch for more papers by this authorR. Varma, R. VarmaSearch for more papers by this author N. Papanikolaou, N. PapanikolaouSearch for more papers by this authorA. Tillisi, A. TillisiSearch for more papers by this authorL. Louay, L. LouaySearch for more papers by this authorM. Singh, M. SinghSearch for more papers by this authorA. Ikomi, A. IkomiSearch for more papers by this authorR. Varma, R. VarmaSearch for more papers by this author First published: 06 October 2011 https://doi.org/10.1016/S0020-7292(09)61110-3Citations: 1AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume107, IssueS2Abstracts of XIX FIGO World Congress of Gynecology and ObstetricsOctober 2009Pages S304-S304 RelatedInformation

The geostationary meteorological satellites (GEO), such as Geostationary Operational Environmental Satellite (GOES), are susceptible to a calibration anomaly around local midnight of the sub-satellite point. A counter measure, the Midnight Blackbody Calibration Correction (MBCC) currently exists at operational level. In this study, the MBCC performance on GOES-11 satellite is characterized with the help of Global Space-based Inter-Calibration System (GSICS) data sets. Results from the comparison of coincident and collocated GSICS-based GOES-11-AIRS data pairs, corresponding to two and half year period from January 2007 through June 2009, reveal that "mid-night residuals" in brightness temperatures persist in all of the GOES-11 Infra-Red (IR) channels, in spite of MBCC. The GOES-11 split window channels (channels 4 and 5) consistently showed significantly large negative (GOES-11-AIRS) biases often reaching values of -1. 5 K or less while the short wave Infra-Red (SWIR) channel (channel 2) produced relatively smaller negative biases (~ -0.3 K or less). Interestingly, the water vapor IR channel (channel 3) exhibits a different pattern from rest of the channels in which consistently opposite biases with small positive (GOES-11-AIRS) difference values (~ 0.3 K or less) could be observed. The reason for the differential behavior of GOES-11 channel 3 is yet to be understood, while it is hypothesized that this might be linked to the convolution algorithm used for matching the AIRS data spectrally with those from GOES water vapor channel. The amount of midnight residuals is shown to have a consistent seasonal dependency, which gets repeated year after year, for the period considered in the analysis.

The Geostationary Operational Environmental Satellite (GOES) program is developing the Advanced Baseline Imager (ABI), a new generation sensor to be carried onboard the GEOS-R satellite (launch expected in 2014). Compared to the current GOES Imager, ABI will have significant advantages for retrieving land surface temperature (LST) as well as providing qualitative and quantitative data for a wide range of applications. The infrared bands of the ABI sensor are designed to achieve a spatial resolution of 2 km at nadir and a noise equivalent temperature of 0.1 K. These improve the imager specifications and compare well with those of polar-orbiting sensors (e.g., Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer). In this paper, we discuss the development of a split window LST algorithm for the ABI sensor. First, we simulated ABI sensor data using the MODTRAN radiative transfer model and NOAA88 atmospheric profiles. To model land conditions, we developed emissivity data for 78 virtual surface types using the surface emissivity library from Snyder Using the simulation results, we performed regression analyses with the candidate LST algorithms. Algorithm coefficients were stratified for dry and moist atmospheres as well as for daytime and nighttime conditions. We estimated the accuracy and sensitivity of each algorithm for different sun-view geometries, emissivity errors, and atmospheric assessments. Finally, we evaluated the most promising algorithm using real data from the GOES-8 Imager and SURFace RADiation Network. The results indicate that the optimized LST algorithm meets the required accuracy (2.3 K) of the GOES-R mission.

A robust and easily implemented verification procedure based on the column-integrated precipitable water (IPW) vapor estimates derived from a network of ground-based global positioning system (GPS) receivers has been used to assess the quality of the Atmospheric Infrared Sounder (AIRS) IPW retrievals over the contiguous United States. For a period of six months from April to October 2004, excellent agreement has been realized between GPS-derived IPW estimates and those determined from AIRS, showing small monthly bias values ranging from 0.5 to 1.5 mm and root-mean-square (rms) differences of 4 turn or less. When the spatial (latitude-longitude) window for the GPS and AIRS matchup observations is reduced from the initial 1/2 degrees by 1/2 degrees to 1/4 degrees by 1/4 degrees, the rms differences are reduced. Analysis revealed that the observed IPW biases between the instruments are strongly correlated to the reported surface pressure differences between the GPS and AIRS observational points. Adjusting the AIRS IPW values to account for the surface pressure discrepancies resulted in significant reductions of the bias between GPS and AIRS. A similar reduction can be obtained by comparing only (GPS-AIRS) match-up pairs for which the corresponding surface pressure differences are 0.5 mb or less. The comparisons also revealed that the AIRS IPW tends to be relatively dry in moist atmospheres (when IPW values >40 mm) but wetter in dry cases (when IPW values <10 mm). This is consistent with the documented bias of satellite measurements toward the first guess used in retrieval algorithms. However, additional study is needed to verify whether the AIRS water vapor retrieval process is the source of the discrepancies. It is shown that the IPW bias and rms differences have a seasonal dependency, with a maximum in summer (bias similar to 1.2 mm, rms similar to 4.14 mm) and minimum in winter (bias < -0.5 mm, rms similar to 3 mm).

The time series of clear‐sky Land Surface Temperatures (LST) for one year, 2001, obtained from pyrgeometric observations at five selected US surface radiation (SURFRAD) stations and independently retrieved for the locations of these stations from Infrared Imager hourly observations of two geostationary satellites, GOES‐8 and GOES‐10, are presented as a sum of time‐dependent expected value (diurnal and seasonal cycles), and weather‐related anomalies. The availability of three independent observations is used to assess random and systematic errors in LST data. Temporal variation of the expected value is approximated as a superposition of the first two annual and diurnal Fourier harmonics. This component of temporal variations of LST absorbs all systematic errors; which themselves are often a subject of diurnal and seasonal variations. The results revealed that the weather‐related temporal variation of LST is much smaller than the temporal variations of the expected value, but much larger than the random errors of observation. Scale of temporal autocorrelation of weather‐related component of clear‐sky LST variations is about 3 days.

Evaluation of satellite land surface temperature (LST) is one of the most difficult tasks in LST retrieval algorithm development, because of spatial and temporal variability of land surface temperature and surface emissivity variations. A large number of high quality "match-up" satellite and ground LST data is needed for the evaluation process. In developing a LST algorithm for the GOES-R Advanced Baseline Imager, we produced a set of "match-up" dataset from SURFace RADiation (SURFRAD) budget network ground measurements and GOES-8 and -10 satellite measurements. The dataset covers one-year GOES Imager data over six SURFRAD sites in the United States. A stringent cloud filtering procedure was applied to minimize cloud contamination in the match-up dataset. Each of the SURFRAD sites contains enough match-up data pairs for ensuring significance of statistical analyses of the LST algorithm. The evaluation was performed by directly and indirectly comparing the SURFRAD and satellite LSTs of each site. The direct comparison was illustrated using scatter plots and histogram plots of the ground and the satellite LSTs, while the indirect comparison was performed using a matrix analysis model developed by Flynn (2006)[1]. We demonstrated that LST measurements from the SURFRAD instrument can be used in our evaluation of the GOES-R LST algorithm development and the precision of the GOES-R LST algorithm can be fairly well estimated.

Although there are a number of sources of radiosonde data for validation of observations from other atmospheric sensors, routine operational sondes remain the main source for a large volume of data. In this study radiosonde moisture profiles are renormalized using Global Positioning System (GPS) Integrated Precipitable Water (IPW) vapor. The GPS‐adjusted radiosonde humidity profiles are then compared to the Atmospheric Infrared Sounder (AIRS) measurements. As a check, AIRS measurements are also compared with unadjusted radiosonde moisture profiles. It is shown that the GPS‐adjusted values are in better agreement with the AIRS measurements. On the basis of this result, the GPS‐adjusted radiosondes are used to assess the AIRS potential accuracy. This is valid because the errors in the AIRS measurements and the adjustments are independent. The GPS‐based renormalization of radiosonde humidity measurements produced a significant improvement in the agreement between AIRS and Vaisala RS 57 H type radiosondes in the lower troposphere, where much of the atmospheric water vapor resides. The adjustment also resulted in improved agreement between AIRS and radiosonde IPW estimates. The results showed a day/night bias in the radiosonde values as compared to the GPS and the AIRS values, demonstrating the potential use of the technique for evaluating and correcting this bias. Established corrections for humidity errors also have been applied to some operational radiosonde observations, specifically the published temperature correction developed for the Vaisala RS80 H type radiosonde. This correction produced a much smaller effect than the GPS adjustment.

The Geostationary Operational Environmental Satellite (GOES) program is developing a new generation sensor, the Advanced Baseline Imager (ABI), to be carried on the GOES-R satellite to be lunched in approximately in 2014. Compared to the current GOES imager, ABI will have significant advantages for measuring land surface temperature as well as to providing qualitative and quantitative data for a wide range of applications. Specifically, spatial resolution of the ABI sensor is 2 km, and the infrared window noise equivalent temperature is 0.1 K, which are very close to the polar-orbiting satellite sensors such as AVHRR. Most importantly, ABI observes the full disk every five minutes, which not only provides more cloud-free measurements but also makes daily temperature variation analysis possible. In this study we developed split window algorithms for the LST measurement from the ABI sensor. We generated the ABI sensor data using MODTRAN radiative transfer model and NOAA88 atmospheric profiles and ran regression analyses for the LST algorithm development. The algorithms are developed by optimizing existing split window LST algorithms and adding a path length correction term to minimize the retrieval errors due to difference atmospheric path absorption from nadir view to the edge-of-scan. The algorithm coefficients are stratified for dry and moist atmospheric conditions, as well as for the daytime and nighttime. The algorithm sensitivity to land surface emissivity uncertainty is analyzed to ensure the algorithm performance.

Summary A new two-stage surgical procedure to treat massive pelvic organ prolapse in elderly women is described. Four women in whom previous pessary treatment had failed (two of these had also tried and failed the double ring pessary) or was not possible due to poor pelvic muscle tone or enlarged vaginal introitus underwent manual reduction of prolapse, colpoperineorrhaphy and ring pessary insertion in the first stage followed by a vaginal hysterectomy with or without sacrospinous fixation 6 weeks later. All patients had a favourable outcome in terms of improvement of symptoms and satisfaction with the treatment.

Integrated Precipitable Water (IPW) vapor estimates derived from a network of ground-based GPS receivers provide an accurate, convenient, and statistically robust means to assess the quality of AIRS water vapor retrievals over the contiguous United States (CONUS). For a period from April to October 2004, GPS IPW estimates were paired with AIRS data nearly coincident in time and space. The matched data pairs exhibit small monthly mean and rms differences, giving confidence in both the AIRS observations and the humidity retrieval. Monthly rms differences were reduced using stricter horizontal matching, indicating that part of the observed differences are attributable to sampling. IPW biases were found to be proportional to surface pressure differences reported for the GPS and AIRS retrievals. IPW match-up pairs for which the surface pressure differences are small (less than 0.5 mb) show smaller biases. Moreover, adjusting the AIRS IPW values to account for the reported surface pressure differences resulted in significant reductions of both bias and rms differences. The AIRS IPW estimates tend to be relatively dry in moist atmospheres (IPW values > 40 mm) and wet in dry cases (IPW values < 10 mm). This is consistent with previously documented tendency of satellite retrievals to be biased towards initial guess used for the retrievals. Additional investigation is necessary to verify and quantify the effect of the bias of AIRS water vapor retrievals towards initial guess on AIRS IPW estimates and their validation. Finally, it is shown that the IPW bias and rms differences appear to have a seasonal dependency.

The NESDIS Office of Research and Applications performs sensor calibration and data product validation (cal/val) for NOAA’s polar and geosynchronous operational environmental satellites, as well as for a number of non-NOAA spacecraft and instruments. This paper summarizes the scope of these efforts, describes some of the unique methods developed and used by ORA and its partners, and presents and discusses selected recent results. Particular attention is paid to the Simultaneous Nadir Overpass (SNO) method for the on-orbit inter-calibration of like sensors on successive iterations of NOAA’s Polar-orbiting Operational Environmental Satellites. Developed to check the channel by channel performance of the High Resolution Infrared Sounder (HIRS) instruments on NOAA-17 and -18, the SNO method has now been applied to test the effectiveness of calibration corrections made to Advanced Very High Resolution Radiometer (AVHRR) observations and to Advanced Microwave Sounding Unit (AMSU) data. The use of a network of surfacebased GPS receivers to determine atmospheric integrated precipitable water vapor (IPW) accurately and Corresponding Author: James G. Yoe 5200 Auth Road, WWB Room 808, Camp Springs, MD, 20746, United States of America. James.G.Yoe@noaa.gov precisely with 30-min temporal resolution now provides an effective and rapid means of validating satellite moisture retrievals. The method has been used successfully to validate observations from the Atmospheric Infrared Sounder (AIRS) and sounders on NOAA’s Geosynchronous Operational Environmental Satellites (GOES). Although the method does not provide a vertical profile of moisture, it is shown to provide an effective scaling constraint for satellite and radiosonde intercomparisons.