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.

Smoke consequences of new wildfire regimes driven by climate change

Abstract: Smoke from wildfires has adverse biological and social consequences, and various lines of evidence suggest that smoke from wildfires in the future may be more intense and widespread, demanding that methods be developed to address its effects on people, ecosystems, and the atmosphere. In this paper, we present the essential ingredients of a modeling system for projecting smoke consequences in a rapidly warming climate that is expected to change wildfire regimes significantly. We describe each component of the system, offer suggestions for the elements of a modeling agenda, and provide some general guidelines for making choices among potential components. We address a prospective audience of researchers whom we expect to be fluent already in building some or many of these components, so we neither prescribe nor advocate particular models or software. Instead, our intent is to highlight fruitful ways of thinking about the task as a whole and its components, while providing substantial, if not exhaustive, documentation from the primary literature as reference. This paper provides a guide to the complexities of smoke modeling under climate change, and a research agenda for developing a modeling system that is equal to the task while being feasible with current resources.

In Situ Aircraft Measurements of the Vertical Distribution of Liquid and Ice Water Content in Midlatitude Mixed-Phase Clouds

Abstract: The vertical distribution of liquid and ice water content and their partitioning is studied using 34 cases of in situ measured microphysical properties in midlatitude mixed-phase clouds, with liquid water path ranging from near zero to ~248 g m−2, total water path ranging from near zero to ~562 g m−2, and cloud-top temperature ranging from −2° to −38°C. The 34 profiles were further divided into three cloud types depending on their vertical extents and altitudes. It is found that both the vertical distribution of liquid water within a cloud and the liquid water fraction (of total condensed water) as a function of temperature or relative position in a cloud layer are cloud-type dependent. In particular, it is found that the partitioning between liquid and ice water for midlevel shallow clouds is relatively independent on the vertical position within the cloud while it clearly depends on cloud mean temperature. For synoptic snow clouds, however, liquid water fraction increases with the decrease of al...

Interactions of nocturnal slope flows with ambient winds

Abstract: A quasi-one-dimensional numerical model containing a prognostic turbulent kinetic energy parameterization and simplified approximations to horizontal gradients is used to study interactions of thermally induced nocturnal slope flows with following and opposing ambient winds. It is found that a following ambient wind causes the peak perturbation wind to be weaker and to be realized at a greater height, while an opposing ambient wind leads to a stronger perturbation wind at a lower height. The reason for this response lies in the interactions of the shears of the thermal and ambient components through the mechanical production of turbulent kinetic energy.

Toward snowfall retrieval over land by combining satellite and in situ measurements

Abstract: [1] Although snowfall is an important component of global precipitation in extratropical regions, satellite snowfall estimate is still in an early developmental stage, and existing satellite remote sensing techniques do not yet provide reliable estimates of snowfall over higher latitudes. Toward the goal of developing a global snowfall algorithm, in this study, a Bayesian technique has been tested for snowfall retrieval over land using high-frequency microwave satellite data. In this algorithm, observational data from satellite- and surface-based radars and in situ aircraft measurements are used to build the a priori database consisting of snowfall profiles and corresponding brightness temperatures. The retrieval algorithm is applied to the Advanced Microwave Sounding Unit-B data for snowfall cases that occurred over the Great Lakes region, and the results are compared with the surface radar data and daily snowfall data collected from National Weather Service stations. Although the algorithm is still at an ad hoc stage, the results show that the satellite retrievals compare well with surface measurements in the early winter season, when there is no accumulated snow on ground. However, for the late winter season, when snow constantly covers the ground, the snowfall retrievals become very noisy and show overestimation. Therefore, it is concluded that developing methods to efficiently remove surface snow cover contamination will be the major task in the future to improve the accuracy of satellite snowfall retrieval over land.

A Blended Satellite Total Precipitable Water Product for Operational Forecasting

Abstract: Total precipitable water (TPW), the amount of water vapor in a column from the surface of the earth to space, is used by forecasters to predict heavy precipitation. In this paper, a process for blending TPW values retrieved from two satellite sources is detailed: the Advanced Microwave Sounding Unit (AMSU) instruments on three NOAA satellites, and the Special Sensor Microwave Imager (SSM/I) instruments on three Defense Meteorological Satellite Program (DMSP) satellites. The process starts with a blending algorithm, which matches the cumulative probability distribution functions of TPW retrievals from the two instruments to lessen their differences. The data are then mapped to a map projection useful to forecasters and composited for 12 h to make a global map. These maps are produced hourly using Data Processing and Error Analysis System (DPEAS) software and made available to forecasters online.