Cooperative Institute for Research in the Atmosphere

About: Cooperative Institute for Research in the Atmosphere is a based out in . It is known for research contribution in the topics: Snow & Data assimilation. The organization has 332 authors who have published 997 publications receiving 38835 citations. The organization is also known as: CIRA.

Freshwater flux to Sermilik Fjord, SE Greenland

Abstract: . Terrestrial inputs of freshwater flux to Sermilik Fjord, SE Greenland, were estimated, indicating ice discharge to be the dominant source of freshwater. A freshwater flux of 40.4 ± 4.9×109 m3 y−1 was found (1999–2008), with an 85% contribution originated from ice discharge (65% alone from Helheim Glacier), 11% from terrestrial surface runoff (from melt water and rain), 3% from precipitation at the fjord surface area, and 1% from subglacial geothermal and frictional melting due to basal ice motion. The results demonstrate the dominance of ice discharge as a primary mechanism for delivering freshwater to Sermilik Fjord. Time series of ice discharge for Helheim Glacier, Midgard Glacier, and Fenris Glacier were calculated from satellite-derived average surface velocity, glacier width, and estimated ice thickness, and fluctuations in terrestrial surface freshwater runoff were simulated based on observed meteorological data. These simulations were compared and bias corrected against independent glacier catchment runoff observations. Modeled runoff to Sermilik Fjord was variable, ranging from 2.9 ± 0.4×109 m3 y−1 in 1999 to 5.9 ± 0.9×109 m3 y−1 in 2005. The sub-catchment runoff of the Helheim Glacier region accounted for 25% of the total runoff to Sermilik Fjord. The runoff distribution from the different sub-catchments suggested a strong influence from the spatial variation in glacier coverage, indicating high runoff volumes, where glacier cover was present at low elevations.

Objective Estimation of the 24-h Probability of Tropical Cyclone Formation

Abstract: A new product for estimating the 24-h probability of TC formation in individual 5 83 58 subregions of the North Atlantic, eastern North Pacific, and western North Pacific tropical basins is developed. This product uses environmental and convective parameters computed from best-track tropical cyclone (TC) positions, National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) analysis fields, and water vapor (;6.7 mm wavelength) imagery from multiple geostationary satellite platforms. The parameters are used in a two-step algorithm applied to the developmental dataset. First, a screening step removes all data points with environmental conditions highly unfavorable to TC formation. Then, a linear discriminant analysis (LDA) is applied to the screened dataset. A probabilistic prediction scheme for TC formation is developed from the results of the LDA. Coefficients computed by the LDA show that the largest contributors to TC formation probability are climatology, 850-hPa circulation, and distance to an existing TC. The product was evaluated by its Brier and relative operating characteristic skill scores and reliability diagrams. These measures show that the algorithmgenerated probabilistic forecasts are skillful with respect to climatology, and that there is relatively good agreement between forecast probabilities and observed frequencies. As such, this prediction scheme has been implemented as an operational product called the National Environmental Satellite, Data, and Information Services (NESDIS) Tropical Cyclone Formation Probability (TCFP) product. The TCFP product updates every 6 h and displays plots of TC formation probability and input parameter values on its Web site. At present, the TCFP provides real-time, objective TC formation guidance used by tropical cyclone forecast offices in the Atlantic, eastern Pacific, and western Pacific basins.

A simple method to improve ensemble‐based ozone forecasts

Abstract: [1] Forecasts from seven air quality models and ozone data collected over the eastern USA and southern Canada during July and August 2004 are used in creating a simple method to improve ensemble-based forecasts of maximum daily 1-hr and 8-hr averaged ozone concentrations. The method minimizes least-square error of ensemble forecasts by assigning weights for its members. The real-time ozone (O3) forecasts from this ensemble of models are statistically evaluated against the ozone observations collected for the AIRNow database comprising more than 350 stations. Application of this method is shown to significantly improve overall statistics (e.g., bias, root mean square error, and index of agreement) of the weighted ensemble compared to the averaged ensemble or any individual ensemble member. If a sufficient number of observations is available, we recommend that weights be calculated daily; if not, a longer training phase will still provide a positive benefit.

Representing Variability in Subgrid Snow Cover and Snow Depth in a Global Land Model: Offline Validation

Abstract: Subgrid snow cover is one of the key parameters in global land models since snow cover has large impacts on the surface energy and moisture budgets, and hence the surface temperature. In this study, the Subgrid Snow Distribution (SSNOWD) snow cover parameterization was incorporated into the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) land surface model. SSNOWD assumes that the subgrid snow water equivalent (SWE) distribution follows a lognormal distribution function, and its parameters are physically derived from geoclimatic information. Two 29-yr global offline simulations, with and without SSNOWD, were performed while forced with the Japanese 25-yr Reanalysis (JRA-25) dataset combined with an observed precipitation dataset. The simulated spatial patterns of mean monthly snow cover fraction were compared with satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The snow cover fraction was improved by the inclusion of SSNOWD, particularly ...