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.

A multi-time-scale four-dimensional variational data assimilation scheme and its application to simulated radial velocity and reflectivity data

Abstract: In this study, a multi-time-scale four-dimensional variational data assimilation (MTS-4DVar) scheme is developed and applied to the assimilation of radar observations. The MTS-4DVar employs...

Spatial, temporal, and interspecies patterns in fine particulate matter in Texas.

Abstract: The Big Bend Regional Aerosol and Visibility Observational (BRAVO) field study was conducted from July to October 1999 and was followed by several years of modeling and data analyses to examine the causes of haze at Big Bend National Park TX (BBNP). During BRAVO, daily speciated fine (diameter <2.5 microm) particulate concentrations were measured at 37 sites throughout Texas. At the primary receptor site, K-Bar Ranch, there were many additional measurements including a "high-sensitivity" version of the 24-hr fine particulate elemental data. The spatial, temporal, and interspecies patterns in these data are examined here to qualitatively investigate source regions and source types influencing the fine particulate concentrations in Texas with an emphasis on sources of sulfates, the largest contributor to fine mass and light extinction. Peak values of particulate sulfur (S) varied spatially and seasonally. Maximum S was in Northeast Texas during the summer, whereas peak S at BBNP was in the fall. Sulfate acidity at BBNP also varied by month. Sources of Se were evident in Northeast Texas and from the Carbon I and II plants. High S episodes at BBNP during BRAVO had several different trace element characteristics. Carbon concentrations at BBNP during BRAVO were probably mostly urban-related, with arrival from the Houston area likely. The Houston artificial tracer released during the second half of BRAVO was highly correlated with some carbon fractions. There was evidence of the influence of African dust at sites throughout Texas during the summer. Patterns in several trace elements were also examined. Vanadium was associated with air masses from Mexico. Lead concentrations in southern Texas have dropped dramatically over the past several years.

A methodology for estimating seasonal cycles of atmospheric CO 2 resulting from terrestrial net ecosystem exchange (NEE) fluxes using the Transcom T3L2 pulse-response functions

Abstract: . We present a method for translating modeled terrestrial net ecosystem exchange (NEE) fluxes of carbon into the corresponding seasonal cycles in atmospheric CO2. The method is based on the pulse-response functions from the Transcom 3 Level 2 (T3L2) atmospheric tracer transport model (ATM) intercomparison. The new pulse-response method is considerably faster than a full forward ATM simulation, allowing CO2 seasonal cycles to be computed in seconds, rather than the days or weeks required for a forward simulation. Further, the results provide an estimate of the range of transport uncertainty across 13 different ATMs associated with the translation of surface NEE fluxes into an atmospheric signal. We evaluate the method against the results of archived forward ATM simulations from T3L2. The latter are also used to estimate the uncertainties associated with oceanic and fossil fuel influences. We present a regional breakdown at selected monitoring sites of the contribution to the atmospheric CO2 cycle from the 11 different T3L2 land regions. A test case of the pulse-response code, forced by NEE fluxes from the Community Land Model, suggests that for many terrestrial models, discrepancies between model results and observed atmospheric CO2 cycles will be large enough to clearly transcend ATM uncertainties.

National Weather Service Severe Weather Warnings as Threats-in-Motion

Abstract: Threats-in-Motion (TIM) is a warning generation approach that would enable the NWS to advance severe thunderstorm and tornado warnings from the current static polygon system to continuously updating polygons that move forward with a storm. This concept is proposed as a first stage for implementation of the Forecasting a Continuum of Environmental Threats (FACETs) paradigm, which eventually aims to deliver rapidly updating probabilistic hazard information alongside NWS warnings, watches, and other products. With TIM, a warning polygon is attached to the threat and moves forward along with it. This provides more uniform, or equitable, lead time for all locations downstream of the event. When forecaster workload is high, storms remain continually tracked and warned. TIM mitigates gaps in warning coverage and improves the handling of storm motion changes. In addition, warnings are automatically cleared from locations where the threat has passed. This all results in greater average lead times and lower average departure times than current NWS warnings, with little to no impact to average false alarm time. This is particularly noteworthy for storms expected to live longer than the average warning duration (30 or 45 min) such as long-tracked supercells that are more prevalent during significant tornado outbreaks.

The Relationship between Deterministic and Ensemble Mean Forecast Errors Revealed by Global and Local Attractor Radii

Abstract: It has been demonstrated that ensemble mean forecasts, in the context of the sample mean, have higher forecasting skill than deterministic (or single) forecasts. However, few studies have focused on quantifying the relationship between their forecast errors, especially in individual prediction cases. Clarification of the characteristics of deterministic and ensemble mean forecasts from the perspective of attractors of dynamical systems has also rarely been involved. In this paper, two attractor statistics—namely, the global and local attractor radii (GAR and LAR, respectively)—are applied to reveal the relationship between deterministic and ensemble mean forecast errors. The practical forecast experiments are implemented in a perfect model scenario with the Lorenz96 model as the numerical results for verification. The sample mean errors of deterministic and ensemble mean forecasts can be expressed by GAR and LAR, respectively, and their ratio is found to approach $$\sqrt 2 $$ with lead time. Meanwhile, the LAR can provide the expected ratio of the ensemble mean and deterministic forecast errors in individual cases.