Modeling and analysis of the variability of the water cycle in the upper Rio Grande basin at high resolution

Estimating the water budgets in a small-scale basin is a challenge, especially in the mountainous western United States, where the terrain is complex and observational data in the mountain areas are sparse. This manuscript reports on research that downscaled 5-yr (1999-2004) hydrometeorological fields over the upper Rio Grande basin from a 2.5° NCEP-NCAR reanalysis to a 4-km local scale using a regional climate model [fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5), version 3]. The model can reproduce the terrain-related precipitation distribution - the trend of diurnal, seasonal, and interannual precipitation variability - although poor snow simulation caused it to overestimate precipitation and evapotranspiration in the cold season. The outcomes from the coupled model are also comparable to offline Variable Infiltration Capacity (VIC) and Land Data Assimilation System (LDAS)/Mosaic land surface simulations that are driven by observed and/or analyzed surface meteorological data. © 2007 American Meteorological Society

Modeling intraseasonal features of 2004 North American monsoon precipitation

This study examines the capabilities and limitations of the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) in predicting the precipitation and circulation features that accompanied the 2004 North American monsoon (NAM). When the model is reinitialized every 5 days to restrain the growth of modeling errors, its results for precipitation checked at subseasonal time scales (not for individual rainfall events) become comparable with ground- and satellite-based observations as well as with the NAM's diagnostic characteristics. The modeled monthly precipitation illustrates the evolution patterns of monsoon rainfall, although it underestimates the rainfall amount and coverage area in comparison with observations. The modeled daily precipitation shows the transition from dry to wet episodes on the monsoon onset day over the Arizona-New Mexico region, and the multiday heavy rainfall (>1 mm day-1) and dry periods after the onset. All these modeling predictions agree with observed variations. The model also accurately simulated the onset and ending dates of four major moisture surges over the Gulf of California during the 2004 monsoon season. The model reproduced the strong diurnal variability of the NAM precipitation, but did not predict the observed diurnal feature of the precipitation peak's shift from the mountains to the coast during local afternoon to late night. In general, the model is able to reproduce the major, critical patterns and dynamic variations of the NAM rainfall at intraseasonal time scales, but still includes errors in precipitation quantity, pattern, and timing. The numerical study suggests that these errors are due largely to deficiencies in the model's cumulus convective parameterization scheme, which is responsible for the model's precipitation generation. © 2007 American Meteorological Society

Discovery of a new supernova remnant G150.3+4.5

Large-scale radio continuum surveys have good potential for discovering new Galactic supernova remnants (SNRs). Surveys of the Galactic plane are often limited in the Galactic latitude of |b| ~ 5 degree. SNRs at high latitudes, such as the Cygnus Loop or CTA~1, cannot be detected by surveys in such limited latitudes. Using the available Urumqi 6 cm Galactic plane survey data, together with the maps from the extended ongoing 6 cm medium latitude survey, we wish to discover new SNRs in a large sky area. We searched for shell-like structures and calculated radio spectra using the Urumqi 6 cm, Effelsberg 11 cm, and 21 cm survey data. Radio polarized emission and evidence in other wavelengths are also examined for the characteristics of SNRs. We discover an enclosed oval-shaped object G150.3+4.5 in the 6 cm survey map. It is about 2.5 degree wide and 3 degree high. Parts of the shell structures can be identified well in the 11 cm, 21 cm, and 73.5 cm observations. The Effelsberg 21 cm total intensity image resembles most of the structures of G150.3+4.5 seen at 6 cm, but the loop is not closed in the northwest. High resolution images at 21 cm and 73.5 cm from the Canadian Galactic Plane Survey confirm the extended emission from the eastern and western shells of G150.3+4.5. We calculated the radio continuum spectral indices of the eastern and western shells, which are $\beta \sim -2.4$ and $\beta \sim -2.7$ between 6 cm and 21 cm, respectively. The shell-like structures and their non-thermal nature strongly suggest that G150.3+4.5 is a shell-type SNR. For other objects in the field of view, G151.4+3.0 and G151.2+2.6, we confirm that the shell-like structure G151.4+3.0 very likely has a SNR origin, while the circular-shaped G151.2+2.6 is an HII region with a flat radio spectrum, associated with optical filamentary structure, H$\alpha$, and infrared emission.Comment: 5 pages, 3 figures, accepted for publication of Astronomy and Astrophysic

Model performance of downscaling 1999-2004 hydrometeorological fields to the upper Rio Grande basin using different forcing datasets

This study downscaled more than five years of data (1999-2004) for hydrometeorological fields over the upper Rio Grande basin (URGB) to a 4-km resolution using a regional model [fifth-generation Pennsylvania State University-National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5, version 3)] and two forcing datasets that include National Centers for Environmental Prediction (NCEP)-NCAR reanalysis-1 (R1) and North America Regional Reanalysis (NARR) data. The long-term high-resolution simulation results show detailed patterns of hydroclimatological fields that are highly related to the characteristics of the regional terrain; the most important of these patterns are precipitation localization features caused by the complex topography. In comparison with station observational data, the downscaling processing, on whichever forcing field is used, generated more accurate surface temperature and humidity fields than the Eta Model and NARR data, although it still included marked errors, such as a negative (positive) bias toward the daily maximum (minimum) temperature and overestimated precipitation, especially in the cold season. Comparing the downscaling results forced by the NARR and R1 with both the gridded and station observational data shows that under the NARR forcing, the MM5 model produced generally better results for precipitation, temperature, and humidity than it did under the R1 forcing. These improvements were more apparent in winter and spring. During the warm season, although the use of NARR improved the precipitation estimates statistically at the regional (basin) scale, it substantially underestimated them over the southern upper Rio Grande basin, partly because the NARR forcing data exhibited warm and dry biases in the monsoon-active region during the simulation period and improper domain selection. Analyses also indicate that over mountainous regions, both the Climate Prediction Center's (CPC's) gridded (0.25°) and NARR forcings underestimate precipitation in comparison with station gauge data. © 2008 American Meteorological Society

Impacts of model calibration on high-latitude land-surface processes: PILPS 2(e) calibration/validation experiments

In the PILPS 2(e) experiment, the Snow Atmosphere Soil Transfer (SAST) land-surface scheme developed from the Biosphere-Atmosphere Transfer Scheme (BATS) showed difficulty in accurately simulating the patterns and quantities of runoff resulting from heavy snowmelt in the high-latitude Torne-Kalix River basin (shared by Sweden and Finland). This difficulty exposes the model deficiency in runoff formations. After representing subsurface runoff and calibrating the parameters, the accuracy of hydrograph prediction improved substantially. However, even with the accurate precipitation and runoff, the predicted soil moisture and its variation were highly "model-dependent". Knowledge obtained from the experiment is discussed. © 2003 Elsevier Science B.V. All rights reserved

Low frequency oscillations in total ozone measurements

Low frequency oscillations with periods of approximately one to two months are found in eight years of global grids of total ozone data from the Total Ozone Mapping Spectrometer (TOMS) satellite instrument. The low frequency oscillations corroborate earlier analyses based on four years of data. In addition, both annual and seasonal one-point correlation maps based on the 8-year TOMS data are presented. The results clearly show a standing dipole in ozone perturbations, oscillating with 35 to 50 day periods over the equatorial Indian Ocean-west Pacific region. This contrasts with the eastward moving dipole reported in other data sets. The standing ozone dipole appears to be a dynamical feature associated with vertical atmospheric motions. Consistent with prior analyses based on lower stratospheric temperature fields, large-scale standing patterns are also found in the extratropics of both hemispheres, correlated with ozone fluctuations over the equatorial west Pacific. In the Northern Hemisphere, a standing pattern is observed extending from the tropical Indian Ocean to the north Pacific, across North America, and down to the equatorial Atlantic Ocean region. This feature is most pronounced in the NH summer

Sensitivity of North American monsoon rainfall to multisource sea surface temperatures in MM5

In this article, four continually processed sea surface temperature (SST) datasets, including the Reynolds SST (RYD), the global final analysis of skin temperature at oceans (FNL), and two Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua SSTs retrieved from thermal infrared imagery (TIR) and midinfrared imagery (MIR), were compared. The results show variations from each other. In comparison with the RYD SST, the FNL data have -0.5° ∼ 0.5°C perturbations, while the TIR and MIR SSTs possess larger deviations of -2° ∼ 1°C, mainly due to algorithm and/or sensor differences in these SST datasets. A regional model, the fifth-generation Pennsylvania State University-Na tional Center for Atmospheric Research (Penn State-NCAR) Mesoscale Model (MM5), was used to investigate whether model atmospheric predictions, especially those concerning precipitation during the North American monsoon season, are sensitive to these SST variations. A comparison of rainfall, atmospheric height, temperature, and wind fields produced by model results, reanalysis data, and observations indicates that, at monthly scale, the model shows changes in the simulations for three consecutive years; in particular, rainfall amounts, timing, and even patterns vary at some specific regions. Forced by the MODIS Aqua midinfrared SST (MIR), which includes large regions with SST values lower than the conventional Reynolds SST, the MM5 rain field predictions show reduced errors over land and oceans compared to when the model is forced by other SST data. Specifically, rainfall estimates are improved over the offshore of southern Mexico, the Gulf of Mexico, the coastal regions of southern and eastern Mexico, and the southwestern U.S. monsoon active region, but only slightly improved over the monsoon core and the high-elevated Great Plains. Using MIR SST data, one is also capable of improving geopotential height and temperature fields in comparison wit he reanalysis data. © 2005 American Meteorological Society

Investigate the impacts of assimilating satellite rainfall estimates on rainstorm forecast over southwest United States

Using the MM5-4DVAR system, a monsoon rainstorm case over southern Arizona (5-6 August 2002) was investigated for the influence of assimilating satellite rainfall estimates on precipitation forecasts. A set of numerical experiments was conducted with multiple configurations including using 20-km or 30-km grid distances and none or 3-h or 6-h assimilation time windows. Results show that satellite rainfall assimilation can improve the rainstorm-forecasting pattern and amount to some extent. The minimization procedure of 4DVAR is sensitive to model spatial resolution and the assimilation time window. The 3-h assimilation window with hourly rainfall data works well for the 6-h forecast, and for 12-h or longer forecasts, a 6-h assimilation window will be requested. Copyright 2004 by the American Geophysical Union

On delayed genetic regulatory networks with polytopic uncertainties: Robust stability analysis

Copyright [2008] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, we investigate the robust asymptotic stability problem of genetic regulatory networks with time-varying delays and polytopic parameter uncertainties. Both cases of differentiable and nondifferentiable time-delays are considered, and the convex polytopic description is utilized to characterize the genetic network model uncertainties. By using a Lyapunov functional approach and linear matrix inequality (LMI) techniques, the stability criteria for the uncertain delayed genetic networks are established in the form of LMIs, which can be readily verified by using standard numerical software. An important feature of the results reported here is that all the stability conditions are dependent on the upper and lower bounds of the delays, which is made possible by using up-to-date techniques for achieving delay dependence. Another feature of the results lies in that a novel Lyapunov functional dependent on the uncertain parameters is utilized, which renders the results to be potentially less conservative than those obtained via a fixed Lyapunov functional for the entire uncertainty domain. A genetic network example is employed to illustrate the applicability and usefulness of the developed theoretical results

Impact of assimilating rainfall derived from radar and satellites on rainstorm forecasts over the Southwestern United States

The impact of assimilating rainfall derived from radar and satellites on rainstorm forecasts over the Southwestern United States is discussed. The major advantage of 4DVAR is the use of full model dynamics and physics to assimilate multiple-time-level observation data. Rainfall assimilation via 4DVAR is used to improve the moisture distributions in model IC. It is found that by using 4DVAR to generate model IC, the precipitation intensity and patterns can be improved substantially over the mid-latitude plain regions