(Table 1) Ice thickness at lake Nam Co, Tibet, during 2008-2009

Lake ice change is one of the sensitive indicators of regional and global climate change. Different sources of data are used in monitoring lake ice phenology nowadays. Visible and Near Infrared bands of imagery (VNIR) are well suited for the observation of freshwater ice change, for example data from AVHRR and MODIS. Active and passive microwave data are also used for the observation of lake ice, e.g., from satellite altimetry and radiometry, backscattering coefficient from QuickSCAT, brightness temperature (Tb) from SSM/I, SMMR, and AMSR-E. Most of the studies are about lake ice cover phenology, while few studies focus on lake ice thickness. For example, Hall et al. using 5 GHz (6 cm) radiometer data showed a good relationship between Tb and ice thickness. Kang et al. found the seasonal evolution of Tb at 10.65 GHz and 18.7 GHz from AMSR-E to be strongly influenced by ice thickness. Many studies on lake ice phenology have been carried out since the 1970s in cold regions, especially in Canada, the USA, Europe, the Arctic, and Antarctica. However, on the Tibetan Plateau, very little research has focused on lake ice-cover change; only a small number of published papers on Qinghai Lake ice observations. The main goal of this study is to investigate the change in lake ice phenology at Nam Co on the Tibetan Plateau using MODIS and AMSR-E data (monitoring the date of freeze onset, the formation of stable ice cover, first appearance of water, and the complete disappearance of ice) during the period 2000-2009.

(Table 3) Air temperatures, and snow and ice characteristics of fresh and brackish water lakes in the Northwest Territories, Canada

The algorithms designed to estimate snow water equivalent (SWE) using passive microwave measurements falter in lake-rich high-latitude environments due to the emission properties of ice covered lakes on low frequency measurements. Microwave emission models have been used to simulate brightness temperatures (Tbs) for snowpack characteristics in terrestrial environments but cannot be applied to snow on lakes because of the differing subsurface emissivities and scattering matrices present in ice. This paper examines the performance of a modified version of the Helsinki University of Technology (HUT) snow emission model that incorporates microwave emission from lake ice and sub-ice water. Inputs to the HUT model include measurements collected over brackish and freshwater lakes north of Inuvik, Northwest Territories, Canada in April 2008, consisting of snowpack (depth, density, and snow water equivalent) and lake ice (thickness and ice type). Coincident airborne radiometer measurements at a resolution of 80x100 m were used as ground-truth to evaluate the simulations. The results indicate that subsurface media are simulated best when utilizing a modeled effective grain size and a 1 mm RMS surface roughness at the ice/water interface compared to using measured grain size and a flat Fresnel reflective surface as input. Simulations at 37 GHz (vertical polarization) produce the best results compared to airborne Tbs, with a Root Mean Square Error (RMSE) of 6.2 K and 7.9 K, as well as Mean Bias Errors (MBEs) of -8.4 K and -8.8 K for brackish and freshwater sites respectively. Freshwater simulations at 6.9 and 19 GHz H exhibited low RMSE (10.53 and 6.15 K respectively) and MBE (-5.37 and 8.36 K respectively) but did not accurately simulate Tb variability (R= -0.15 and 0.01 respectively). Over brackish water, 6.9 GHz simulations had poor agreement with airborne Tbs, while 19 GHz V exhibited a low RMSE (6.15 K), MBE (-4.52 K) and improved relative agreement to airborne measurements (R = 0.47). Salinity considerations reduced 6.9 GHz errors substantially, with a drop in RMSE from 51.48 K and 57.18 K for H and V polarizations respectively, to 26.2 K and 31.6 K, although Tb variability was not well simulated. With best results at 37 GHz, HUT simulations exhibit the potential to track Tb evolution, and therefore SWE through the winter season.

(Table 1) Mean temperatures in january and july, and total annual snowfall at Great Bear Lake and Great Slave Lake, Canada

The sensitivity of brightness temperature (T(B)) at 6.9, 10.7, and 18.7 GHz from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) observations is investigated over five winter seasons (2002-2007) on Great Bear Lake and Great Slave Lake, Northwest Territories, Canada. The T(B) measurements are compared to ice thicknesses obtained with a previously validated thermodynamic lake ice model. Lake ice thickness is found to explain much of the increase of T(B) at 10.7 and 18.7 GHz. T(B) acquired at 18.7 GHz (V-pol) and 10.7 GHz (H-pol) shows the strongest relation with simulated lake ice thickness over the period of study (R**2 > 0.90). A comparison of the seasonal evolution of T(B) for a cold winter (2003-2004) and a warm winter (2005-2006) reveals that the relationship between T(B) and ice growth is stronger in the cold winter (2003-2004). Overall, this letter shows the high sensitivity of T(B) to ice growth and, thus, the potential of AMSR-E mid-frequency channels to estimate ice thickness on large northern lakes.

Seasonal mean air temperatures and ice phenology characteristics of Great Bear Lake and Great Slave Lake (2002-2009)

Time series of brightness temperatures (T(B)) from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) are examined to determine ice phenology variables on the two largest lakes of northern Canada: Great Bear Lake (GBL) and Great Slave Lake (GSL). T(B) measurements from the 18.7, 23.8, 36.5, and 89.0 GHz channels (H- and V- polarization) are compared to assess their potential for detecting freeze-onset/melt-onset and ice-on/ice-off dates on both lakes. The 18.7 GHz (H-pol) channel is found to be the most suitable for estimating these ice dates as well as the duration of the ice cover and ice-free seasons. A new algorithm is proposed using this channel and applied to map all ice phenology variables on GBL and GSL over seven ice seasons (2002-2009). Analysis of the spatio-temporal patterns of each variable at the pixel level reveals that: (1) both freeze-onset and ice-on dates occur on average about one week earlier on GBL than on GSL (Day of Year (DY) 318 and 333 for GBL; DY 328 and 343 for GSL); (2) the freeze-up process or freeze duration (freeze-onset to ice-on) takes a slightly longer amount of time on GBL than on GSL (about 1 week on average); (3) melt-onset and ice-off dates occur on average one week and approximately four weeks later, respectively, on GBL (DY 143 and 183 for GBL; DY 135 and 157 for GSL); (4) the break-up process or melt duration (melt-onset to ice-off) lasts on average about three weeks longer on GBL; and (5) ice cover duration estimated from each individual pixel is on average about three weeks longer on GBL compared to its more southern counterpart, GSL. A comparison of dates for several ice phenology variables derived from other satellite remote sensing products (e.g. NOAA Interactive Multisensor Snow and Ice Mapping System (IMS), QuikSCAT, and Canadian Ice Service Database) show that, despite its relatively coarse spatial resolution, AMSR-E 18.7 GHz provides a viable means for monitoring of ice phenology on large northern lakes.

Oéuvres diverses de M. Thomas : tome premier.

Contiene: "Éloge de Maurice, Comte de Saxe...", "Éloge de Henri-François D'Auguessau, Chancelier de France...", "Éloge de René Diguay-Trouin ...", "Éloge de Maximilien de Bethune, Duc de Sully...", con portadillas propias.

School of Nursing Class of 1984

Top Row: Suzanne P. Zeros, Leslie A. Hazle, Deborah L. Thar, Jo-Ann Uhrhammer, Susan M. Revesz, Karla M. Jackson, Laura L. Campbell, Carol T. Dekeyser, Jeanette R. Lewey, Constance B. Squibb, Kristen Eckoff, Martha J. Armantrout, Kathleen A. Duhart, Sara J. Hemming, Carrie L. Malroit, Anne Marie L. Piehl, Rita A. Dobry, Susan A. Wintermeyer

Row 2: Deborah L. Kurzeja, Elanie C. Jenkins, Mary Nehra, June Ellis, Lisa Mediodia, Mary G. Rutz, Diane L. Larson, Mark A. Kempton, Margaret M. Ulchaker, Maureen B. Schreibea, Jan E. Merrick, Holly Russell, Betsy J. hodgman, Maeve N. Boran, Theresa J. Coker, Lisa Moss, Nancy J. Deckert, Nancy R. Bailey

Row 3: Denise M. Zapinski, Michelle M. Post, Elicia baker-Rogers, Lisa A. Mast, Patricia Thomas, Karen A. Bartoluzzi, Jennifer M. Dzieciuch, Margie Von Berge, Nancy Lutz, Pamela Mrstik

Row 4: Elizabeth Doheny, Jacqueline T. Bartone, Lisa A. Pfahler, Sheryl L. Lovelace, Elizabeth A. Bazur, Janet L. Bauman, Delynn M. Dindoffer, Rebecca Waldo

Row 5: Janarl L. Harris, Jeanne M. Cancilla, Amy Garon, Alisa D. Karp, Liz Buchanan, Linda M. Ford.

Row 6: Ondreya Dillard, Linda C. Parks, Tricia Berner, Loranie A. McKaig, Susan M. Bleasdale, Heather L. Colquhoun, Valerie M. Spotts, Marcia L. Fouts

Row 7: Theresa Glick, Carrie L Giltrow, Lisa E. Chapelle, Mary H. Kiledo, Jody Kazmierczak, Patricia E. Goerke, Lisa Weingart, Laura A. Rhead, Pauletta McKivens, Nancy K. Dryer

Row 8: Mary S. Mac Taggart, Lynn M. Stephens, Ann E. Dowling, Amy L. Huntzinger, Patricia A. Schremser, Kathy Hughes, Sally Sample, Cheryl E. Easley, Rhetaugh Dumas, Janice Lindberg, Susan Boehm, Heather Hossack, Susan E. Parry, Amy D. Landau, Michele Mansour, Nancy R. Clark, Sarah Cunningham

Row 9: Rhonda B. Dean, Sandra s. Klein, Cheryl L. Goddard, Toni Rene Dawson, Sara R. Farhat, Lisa M. Kane, Kaye M. Lewandowski, Jennifer A. Blashill, Susan L. Bradley, Mary McGuiness, Ann Dameron, Karolyn L. Maron, Debra Fisher, Rebecca Vredenburg, Elaine B. Fritz, Mary A. Alphenaar, Kathy Rentenbach, Barbara J. Wolff

Row 10: Nancy L. Minegar, Mary E. Conners, Susan E. Kuzma, M. Maureen O'Conner, Elaine P. Wynter, Catherine L. Martin, Bobbi L Hall, Dawn M. Gilbert, Karen M. Kuhn, Genevieve M. Mccarthy, Anne M. Venturi, Jena Bargon, Karen Coesens, Lynne V. Duguay, Barbara A. Sterne, Jill A. Schafer, Jill A. Webster, Katharina E. Smith, Mary K. Brown