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.

Assessment of straylight correction performance for the VIIRS Day/Night Band using Dome-C and Greenland under lunar illumination

Abstract: The day/night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (Suomi-NPP) represents a major advancement in night-time imaging capabilities. However, the DNB is sensitive to noise introduced from straylight, which appears as a grey haze in radiance images. This effect on the DNB is caused by solar illumination entering the optical path when the satellite passes through the day-night terminator projected on the Earth’s surface. It results in an overall increase in the recorded radiance values. This effect is more significant during solstice. Straylight correction techniques have been implemented to remove this unwanted effect. This study presents an effective method to assess straylight correction performance for VIIRS DNB using DNB observations over Dome C in the Antarctic and Greenland under lunar illumination. Nadir observations of these high-latitude regions by VIIRS are selected during the perpetual night season over variou...

Use of the GOES-R Split-Window Difference to Diagnose Deepening Low-Level Water Vapor

Abstract: The depth of boundary layer water vapor plays a critical role in convective cloud formation in the warm season, but numerical models often struggle with accurate predictions of above-surface moisture. Satellite retrievals of water vapor have been developed, but they are limited by the use of a model’s first guess, instrument spectral resolution, horizontal footprint size, and vertical resolution. In 2016, Geostationary Operational Environmental Satellite-R (GOES-R), the first in a series of new-generation geostationary satellites, will be launched. Its Advanced Baseline Imager will provide unprecedented spectral, spatial, and temporal resolution. Among the bands are two centered at 10.35 and 12.3 μm. The brightness temperature difference between these bands is referred to as the split-window difference, and has been shown to provide information about atmospheric column water vapor. In this paper, the split-window difference is reexamined from the perspective of GOES-R and radiative transfer model ...

Quantifying the radiative impact of clouds on tropopause layer cooling in tropical cyclones

Abstract: A ubiquitous cold signal near the tropopause, here called “tropopause layer cooling” (TLC), has been documented in deep convective regions such as tropical cyclones (TCs). Temperature retri...

An Evaluation of a Hybrid, Terrain-Following Vertical Coordinate in the WRF-Based RAP and HRRR Models

Abstract: A new hybrid, sigma-pressure vertical coordinate was recently added to the Weather Research and Forecasting (WRF) Model in an effort to reduce numerical noise in the model equations near co...

Radar rainfall uncertainty modelling influenced by wind

Abstract: Radar-based estimates of rainfall are affected by many sources of uncertainties, which would propagate through the hydrological model when radar rainfall estimates are used as input or initial conditions An elegant solution to quantify these uncertainties is to model the empirical relationship between radar measurements and rain gauge observations (as the ‘ground reference’) However, most current studies only use a fixed and uniform model to represent the uncertainty of radar rainfall, without consideration of its variation under different synoptic regimes Wind is such a typical weather factor, as it not only induces error in rain gauge measurements but also causes the raindrops observed by weather radar to drift when they reach the ground For this reason, as a first attempt, this study introduces the wind field into the uncertainty model and designs the radar rainfall uncertainty model under different wind conditions We separate the original dataset into three subsamples according to wind speed, which are named as WDI (0–2 m/s), WDII (2–4 m/s) and WDIII (>4 m/s) The multivariate distributed ensemble generator is introduced and established for each subsample Thirty typical events (10 at each wind range) are selected to explore the behaviours of uncertainty under different wind ranges In each time step, 500 ensemble members are generated, and the values of 5th to 95th percentile values are used to produce the uncertainty bands Two basic features of uncertainty bands, namely dispersion and ensemble bias, increase significantly with the growth of wind speed, demonstrating that wind speed plays a considerable role in influencing the behaviour of the uncertainty band On the basis of these pieces of evidence, we conclude that the radar rainfall uncertainty model established under different wind conditions should be more realistic in representing the radar rainfall uncertainty This study is only a start in incorporating synoptic regimes into rainfall uncertainty analysis, and a great deal of more effort is still needed to build a realistic and comprehensive uncertainty model for radar rainfall data Copyright © 2014 John Wiley & Sons, Ltd