Towards the GEOSAT Follow-On Precise Orbit Determination Goals of High Accuracy and Near-Real-Time Processing

The US Navy's GEOSAT Follow-On spacecraft (GFO) primary mission objective is to map the oceans using a radar altimeter. Satellite laser ranging data, especially in combination with altimeter crossover data, offer the only means of determining high-quality precise orbits. Two tuned gravity models, PGS7727 and PGS7777b, were created at NASA GSFC for GFO that reduce the predicted radial orbit through degree 70 to 13.7 and 10.0 mm. A macromodel was developed to model the nonconservative forces and the SLR spacecraft measurement offset was adjusted to remove a mean bias. Using these improved models, satellite-ranging data, altimeter crossover data, and Doppler data are used to compute both daily medium precision orbits with a latency of less than 24 hours. Final precise orbits are also computed using these tracking data and exported with a latency of three to four weeks to NOAA for use on the GFO Geophysical Data Records (GDR s). The estimated orbit precision of the daily orbits is between 10 and 20 cm, whereas the precise orbits have a precision of 5 cm

SLR Station Recovery, Center of Frame Motion, and Time Varying Gravity

Weekly station position estimates, beginning with 1993, are derived from the ITRF2008-based SLR processing of up to four satellites: Lageos 1, Lageos2, Starlette, and Stella. Helmert parameters obtained from c omparison of weekly SLR station positions and the a-priori SLRF2008 station complement are evaluated for geocenter motion and scale. Two me thods for modeling time varying gravity are employed in the SLR satel lite POD processing, with GGM03S serving as the static gravity field. Both methods forward model atmosphere gravity derived from 6-hour ECM WF pressure data. The standard approach applies an annual 20x20 field estimated from 4 years of GRACE data, and the IERS2003 recommended linear rates for C20, C30, C40, C21, and S21. The alternate approach us es a new set of low-order/degree 4x4 coefficients estimated weekly fr om SLR & DORIS processing to 10 satellites from 1993-2012. This exper imental tvg4x4 model has been shown to improve the TOPEX, Jason-1, and Jason-2 altimeter satellite orbits,. In this paper we apply the more detailed time-variable gravity modeling to the SLR satellite POD pro cessing and subsequent reference frame analyses. For this study we will evaluate the orbit differences (periodic and secular) for the satel lites concerned, characterize the impact on the station coordinate solutions, and the impact on reference frame parameters (geocenter and s cale)

The Grizzly, December 9, 1983

Ursinus Continues Excellence in Chemistry • Letters to the Editor • Heads\u27 Album Hits Charts • Aquabears Triumphant in Warrender\u27s Last Meet • Women\u27s Basketball Hustles to Two Victories • Mercer Wins Fencing Tourney • Thoma Leads Bears Over Haverfordhttps://digitalcommons.ursinus.edu/grizzlynews/1110/thumbnail.jp

The Impact of Temporal Geopotential Variations on GPS

Lemoine et al. (2006) and Lemoine et al. (2010) showed that applying more detailed models of time-variable gravity (TVG) improved the quality of the altimeter satellite orbits (e.g. TOPEX/Poseidon, Jason-1, Jason-2). This modeling include application of atmospheric gravity derived from 6-hrly pressure fields obtained from the ECMWF and annual gravity variations to degree & order 20x20 in spherical harmonics derived from GRACE data. This approach allowed the development of a consistent geophysical model for application to altimeter satellite orbit determination from 1993 to 2011. In addition, we have also evaluated the impact of TVG modeling on the POD of Jason-1 and Jason-2 by application of a weekly degree & order four gravity coefficient time series developed using data from ten SLR & DORIS-tracked satellites from 1993 to 2011 (Lemoine et al., 2011)

The Grizzly, March 23, 1984

Assault Attempt on Ursinus Female • Speaker Gives Business Pointers • Forced Bussing in Wismer • New Course for Comm Arts Minor • Letters to the Editor • 1984 Fraternity Pledging Ends • New Fine Arts Course Planned for Fall • In my Opinion: Pledging Should be Banned • Meistersingers Give Return Performance • UC Sponsors Science Competition • Dr. Clouser Delivers Goethe Lecture • Lift-A-Thon Raises Funds for New Equipment • Bear Blades Remain Undefeated • Housing Changes Imminent • Orchestra Presents Bach Festival • Aquabears Conclude Best Season Ever • Batsmen Victorious in Opener • Men\u27s Lacrosse Optimistic • Track Team Looking Solidhttps://digitalcommons.ursinus.edu/grizzlynews/1115/thumbnail.jp

Global and Local Gravity Field Models of the Moon Using GRAIL Primary and Extended Mission Data

The Gravity Recovery and Interior Laboratory (GRAIL) mission was designed to map the structure of the lunar interior from crust to core and to advance the understanding of the Moon's thermal evolution by producing a high-quality, high-resolution map of the gravitational field of the Moon. The mission consisted of two spacecraft, which were launched in September 2011 on a Discovery-class NASA mission. Ka-band tracking between the two satellites was the single science instrument, augmented by tracking from Earth using the Deep Space Network (DSN)

High-degree Gravity Models from GRAIL Primary Mission Data

We have analyzed Kaband range rate (KBRR) and Deep Space Network (DSN) data from the Gravity Recovery and Interior Laboratory (GRAIL) primary mission (1 March to 29 May 2012) to derive gravity models of the Moon to degree 420, 540, and 660 in spherical harmonics. For these models, GRGM420A, GRGM540A, and GRGM660PRIM, a Kaula constraint was applied only beyond degree 330. Variancecomponent estimation (VCE) was used to adjust the a priori weights and obtain a calibrated error covariance. The global rootmeansquare error in the gravity anomalies computed from the error covariance to 320320 is 0.77 mGal, compared to 29.0 mGal with the preGRAIL model derived with the SELENE mission data, SGM150J, only to 140140. The global correlations with the Lunar Orbiter Laser Altimeterderived topography are larger than 0.985 between l = 120 and 330. The freeair gravity anomalies, especially over the lunar farside, display a dramatic increase in detail compared to the preGRAIL models (SGM150J and LP150Q) and, through degree 320, are free of the orbittrackrelated artifacts present in the earlier models. For GRAIL, we obtain an a posteriori fit to the Sband DSN data of 0.13 mm/s. The a posteriori fits to the KBRR data range from 0.08 to 1.5 micrometers/s for GRGM420A and from 0.03 to 0.06 micrometers/s for GRGM660PRIM. Using the GRAIL data, we obtain solutions for the degree 2 Love numbers, k20=0.024615+/-0.0000914, k21=0.023915+/-0.0000132, and k22=0.024852+/-0.0000167, and a preliminary solution for the k30 Love number of k30=0.00734+/-0.0015, where the Love number error sigmas are those obtained with VCE

Methods for specifying the target difference in a randomised controlled trial : the Difference ELicitation in TriAls (DELTA) systematic review

Peer reviewedPublisher PD

Using Expression and Genotype to Predict Drug Response in Yeast

Personalized, or genomic, medicine entails tailoring pharmacological therapies according to individual genetic variation at genomic loci encoding proteins in drug-response pathways. It has been previously shown that steady-state mRNA expression can be used to predict the drug response (i.e., sensitivity or resistance) of non-genotyped mammalian cancer cell lines to chemotherapeutic agents. In a real-world setting, clinicians would have access to both steady-state expression levels of patient tissue(s) and a patient's genotypic profile, and yet the predictive power of transcripts versus markers is not well understood. We have previously shown that a collection of genotyped and expression-profiled yeast strains can provide a model for personalized medicine. Here we compare the predictive power of 6,229 steady-state mRNA transcript levels and 2,894 genotyped markers using a pattern recognition algorithm. We were able to predict with over 70% accuracy the drug sensitivity of 104 individual genotyped yeast strains derived from a cross between a laboratory strain and a wild isolate. We observe that, independently of drug mechanism of action, both transcripts and markers can accurately predict drug response. Marker-based prediction is usually more accurate than transcript-based prediction, likely reflecting the genetic determination of gene expression in this cross

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead