Cooperative Institute for Research in the Atmosphere

Fort Collins, CO, United States

Cooperative Institute for Research in the Atmosphere

Fort Collins, CO, United States
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Knaff J.A.,National Oceanic and Atmospheric Administration | Sampson C.R.,U.S. Navy | Chirokova G.,Cooperative Institute for Research in the Atmosphere
Weather and Forecasting | Year: 2017

Forecasts of tropical cyclone (TC) surface wind structure have recently begun to show some skill, but the number of reliable forecast tools, mostly regional hurricane and select global models, remains limited. To provide additional wind structure guidance, this work presents the development of a statistical-dynamical method to predict tropical cyclone wind structure in terms of wind radii, which are defined as the maximum extent of the 34-, 50-, and 64-kt (1 kt = 0.514 m s-1) winds in geographical quadrants about the center of the storm. The basis for TC size variations is developed from an infrared satellite-based record of TC size, which is homogenously calculated from a global sample. The change in TC size is predicted using a statistical-dynamical approach where predictors are based on environmental diagnostics derived from global model forecasts and observed storm conditions. Once the TC size has been predicted, the forecast intensity and track are used along with a parametric wind model to estimate the resulting wind radii. To provide additional guidance for applications and users that require forecasts of central pressure, a wind-pressure relationship that is a function of TC motion, intensity, wind radii (i.e., size), and latitude is then applied to these forecasts. This forecast method compares well with similar wind structure forecasts made by global forecast and regional hurricane models and when these forecasts are used as a member of a simple consensus; its inclusion improves the forecast performance of the consensus. © 2017 American Meteorological Society.

Nachamkin J.E.,U.S. Navy | Jin Y.,U.S. Navy | Grasso L.D.,Cooperative Institute for Research in the Atmosphere | Richardson K.,U.S. Navy
Journal of Applied Meteorology and Climatology | Year: 2017

Cloud-top verification is inherently difficult because of large uncertainties in the estimates of observed cloud-top height. Misplacement of cloud top associated with transmittance through optically thin cirrus is one of the most common problems. Forward radiative models permit a direct comparison of predicted and observed radiance, but uncertainties in the vertical position of clouds remain. In this work, synthetic brightness temperatures are compared with forecast cloud-top heights so as to investigate potential errors and develop filters to remove optically thin ice clouds. Results from a statistical analysis reveal that up to 50% of the clouds with brightness temperatures as high as 280 K are actually optically thin cirrus. The filters successfully removed most of the thin ice clouds, allowing for the diagnosis of very specific errors. The results indicate a strong negative bias in midtropospheric cloud cover in the model, as well as a lack of land-based convective cumuliform clouds. The model also predicted an area of persistent stratus over the North Atlantic Ocean that was not apparent in the observations. In contrast, high cloud tops associated with deep convection were well simulated, as were mesoscale areas of enhanced trade cumulus coverage in the Sargasso Sea. © 2017 American Meteorological Society.

Grasso L.D.,Cooperative Institute for Research in the Atmosphere | Lindsey D.T.,National Oceanic and Atmospheric Administration
International Journal of Remote Sensing | Year: 2011

In preparation for the launch of the next generation of geostationary satellites, considerable effort has been placed on developing new products and algorithms for operational purposes. In addition to satellite-based products and algorithms, satellite imagery can be used to evaluate numerical weather prediction models. Important first steps have already been undertaken to produce synthetic satellite imagery from numerical model output. By comparing synthetic imagery with observed imagery, model performance can be evaluated with a relatively new metric. In this paper, synthetic Geostationary Operational Environmental Satellite (GOES)-12 imagery was used to improve the two-moment prediction of pristine ice in the RAMS (Regional Atmospheric Modeling System) mesoscale model. A thunderstorm event that occurred on 27 June 2005 over the central plains of the USA was chosen for study. Synthetic GOES-12 3.9 μm imagery of RAMS output was compared with observed GOES-12 3.9 μm imagery. A discrepancy between brightness temperatures of two anvils of thunderstorms led to an improvement in the prediction of pristine ice number concentrations. After the model was re-run, subsequent synthetic GOES-12 3.9 μm imagery of one anvil exhibited an improvement compared with observed imagery. Brightness temperatures of the second anvil became too warm, an issue that may be related to model-specified cloud condensation nuclei (CCN) concentrations. This example highlights the potential importance of using synthetic imagery to evaluate numerical weather prediction models. © 2011 Taylor & Francis.

Mitrescu C.,U.S. Navy | L'Ecuyer T.,Colorado State University | Haynes J.,Monash University | Miller S.,Cooperative Institute for Research in the Atmosphere | Turk J.,Jet Propulsion Laboratory
Journal of Applied Meteorology and Climatology | Year: 2010

Identifying and quantifying the intensity of light precipitation at global scales is still a difficult problem for most of the remote sensing algorithms in use today. The variety of techniques and algorithms employed for such a task yields a rather wide spectrum of possible values for a given precipitation event, further hampering the understanding of cloud processes within the climate. The ability of CloudSat's millimeter-wavelength Cloud Profiling Radar (CPR) to profile not only cloud particles but also light precipitation brings some hope to the above problems. Introduced as version zero, the present work uses basic concepts of detection and retrieval of light precipitation using spaceborne radars. Based on physical principles of remote sensing, the radar model relies on the description of clouds and rain particles in terms of a drop size distribution function. Use of a numerical model temperature and humidity profile ensures the coexistence of mixed phases otherwise undetected by the CPR. It also provides grounds for evaluating atmospheric attenuation, important at this frequency. Related to the total attenuation, the surface response is used as an additional constraint in the retrieval algorithm. Practical application of the profiling algorithm includes a 1-yr preliminary analysis of global rainfall incidence and intensity. These results underscore once more the role of CloudSat rainfall products for improving and enhancing current estimates of global light rainfall, mostly at higher latitudes, with the goal of understanding its role in the global energy and water cycle. © 2010 American Meteorological Society.

Bouali M.,National Oceanic and Atmospheric Administration | Bouali M.,Cooperative Institute for Research in the Atmosphere | Ignatov A.,Cooperative Institute for Research in the Atmosphere
Journal of Atmospheric and Oceanic Technology | Year: 2014

The Suomi National Polar-Orbiting Partnership (S-NPP) satellite was successfully launched on 28 October 2011. It carries five new-generation instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS). The VIIRS is a whiskbroom radiometer that scans the surface of the earth using a rotating telescope assembly, a double-sided half-angle mirror, and 16 individual detectors. Substantial efforts are being made to accurately calibrate all detectors in orbit.As of this writing,VIIRS striping is reduced to levels below those seen in corresponding Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) bands and meets the program specifications and requirements. However, the level 2 SST products derived from level 1 sensor data records (SDRs) thermal emissive bands still show residual striping. These artifacts reduce the accuracy of SST measurements and adversely affect cloud masking and the output of downstream applications, such as thermal front detection. To improve the quality of SST imagery derived from theVIIRS sensor, an adaptive algorithm was developed for operational use within the National Environmental Satellite, Data, and Information Service (NESDIS)'s SST system. The methodology uses a unidirectional quadratic variational model to extract stripe noise from the observed image prior to nonlocal filtering. Evaluation of the algorithm performance over an extended dataset demonstrates a significant improvement in the Advanced Clear-Sky Processor for Oceans (ACSPO) VIIRS SST image quality, with normalized improvement factors (NIF) varying between 5%and 25%. ©2014 American Meteorological Society.

Noh Y.-J.,Colorado State University | Seaman .C.J.,Colorado State University | Vonder Haar .T.H.,Cooperative Institute for Research in the Atmosphere | Vonder Haar .T.H.,Colorado State University | Liu G.,Florida State University
Journal of Applied Meteorology and Climatology | Year: 2013

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 isfound 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 altitude within the cloud. While the liquid water fraction in synoptic clouds also decreases with lowering temperature, its magnitude is only about 50% near 0°C. 1. © 2013 American Meteorological Society.

Gallo K.,The Center for Satellite Applications and Research | Hale R.,Cooperative Institute for Research in the Atmosphere | Tarpley D.,Short and Associates Inc. | Yu Y.,The Center for Satellite Applications and Research
Journal of Applied Meteorology and Climatology | Year: 2011

Clear and cloudy daytime comparisons of land surface temperature (LST) and air temperature (Tair) were made for 14 stations included in the U.S. Climate Reference Network (USCRN) of stations from observations made from 2003 through 2008. Generally, LST was greater than Tair for both the clear and cloudy conditions; however, the differences between LST and Tair were significantly less for the cloudy-sky conditions. In addition, the relationships between LST and Tair displayed less variability under the cloudy-sky conditions than under clear-sky conditions. Wind speed, time of the observation of Tair and LST, season, the occurrence of precipitation at the time of observation, and normalized difference vegetation index values were all considered in the evaluation of the relationship between Tair and LST. Mean differences between LST and Tair of less than 2°C were observed under cloudy conditions for the stations, as compared with a minimum difference of greater than 2°C (and as great as 7+°C) for the clear-sky conditions. Under cloudy conditions, Tair alone explained over 94%-and as great as 98%-of the variance observed in LST for the stations included in this analysis, as compared with a range of 81%-93% for clear-sky conditions. Because of the relatively homogeneous land surface characteristics encouraged in the immediate vicinity of USCRN stations, and potential regional differences in surface features that might influence the observed relationships, additional analyses of the relationships between LST and Tair for additional regions and land surface conditions are recommended. © 2011 American Meteorological Society.

Liang Z.,University of Chinese Academy of Sciences | Lu C.,Cooperative Institute for Research in the Atmosphere | Lu C.,National Oceanic and Atmospheric Administration | Tollerud E.I.,University of Chinese Academy of Sciences
Quarterly Journal of the Royal Meteorological Society | Year: 2010

AMeiyu front accompanied by a band of heavy precipitation in East Asia is typically characterized by a much larger moisture gradient than temperature gradient. Many previous studies have suggested use of equivalent potential temperature as a thermodynamic variable under this circumstance. However, dynamic variables coupled with such a thermodynamic variable, e.g. a derived moist potential vorticity (MPV) based on equivalent potential temperature, does not provide useful dynamic insight into these systems. In this study, generalized moist potential vorticity (GMPV) is derived based on a generalized form of potential temperature. Diagnoses of numerical simulations for three typical Meiyu rainfall events show that GMPV provides remarkably accurate tracking of rainfall location, suggesting its potential use as a dynamic tracer for heavy rainfall events such as Meiyu rain bands. © 2010 Royal Meteorological Society.

Miller S.D.,Cooperative Institute for Research in the Atmosphere | Combs C.L.,Cooperative Institute for Research in the Atmosphere | Kidder S.Q.,Cooperative Institute for Research in the Atmosphere | Lee T.F.,U.S. Navy
Journal of Atmospheric and Oceanic Technology | Year: 2012

The next-generation U.S. polar-orbiting environmental satellite program, the Joint Polar Satellite System (JPSS), promises unprecedented capabilities for nighttime remote sensing by way of the day/night band (DNB) low-light visible sensor. The DNB will use moonlight illumination to characterize properties of the atmosphere and surface that conventionally have been limited to daytime observations. Since the moon is a highly variable source of visible light, an important question is where and when various levels of lunar illumination will be available. Here, nighttime moonlight availability was examined based on simulations done in the context of Visible/Infrared Imager Radiometer Suite (VIIRS)/DNB coverage and sensitivity. Results indicate that roughly 45% of all JPSS-orbit [sun-synchronous, 1330 local equatorial crossing time on the ascending node (LTAN)] nighttime observations in the tropics and midlatitudes would provide levels of moonlight at crescent moon or greater. Two other orbits, 1730 and 2130 LTAN, were also considered. The inclusion of a 2130 LTAN satellite would provide similar availability to 1330 LTAN in terms of total moonlit nights, but with approximately a third of those nights being additional because of this orbit's capture of a different portion of the lunar cycle. Nighttime availability is highly variable for near-terminator orbits.A1-h shift from the 1730LTANnear-terminator orbit to 1630LTANwould nearly double the nighttime availability globally from this orbit, including expanded availability at midlatitudes. In contrast, a later shift to 1830 LTAN has a negligible effect. The results are intended to provide high-level guidance for mission planners, algorithm developers, and various users of low-light applications from these future satellite programs.© 2012 American Meteorological Society.

News Article | November 17, 2016

Scientists at Colorado State University are at the forefront of developing new tools and products in support of the satellite mission FORT COLLINS, COLORADO - Scheduled for launch from Cape Canaveral Nov. 19, the nation's newest weather satellite, GOES-R, promises to revolutionize how researchers and forecasters see the Earth from space. Scientists at Colorado State University are at the forefront of developing new tools and products in support of the upcoming mission. The Geostationary Operational Environmental Satellite (GOES) program, a joint venture of NASA and NOAA, constitutes the most commonly used, and arguably most important instruments for observing and forecasting weather. Currently, two GOES spacecraft are orbiting the planet from about 22,000 miles away, a carefully selected distance at which the spacecraft's orbital velocity matches the rotation of the Earth. From the perspective of a viewer on Earth, the satellite appears to hover, giving it a stationary (hence "geostationary") view of our ever-changing weather. The next-generation GOES-R satellite will include several new science instruments for Earth observation from the geostationary orbit, including the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The current GOES series of satellites observes the Earth at five different spectral bands, called "channels," of energy - one channel covers sunlight reflection, and four channels probe different levels of thermal radiation emitted by the Earth's surface and atmosphere. The new ABI instrument on GOES-R will provide 16 channels, giving scientists far more information about the Earth and its weather. This includes, for the first time in over 50 years, a true-color picture of the planet, compared to the black-and-white imagery from the current GOES series. Moreover, the new ABI instrument senses the Earth in much higher definition - up to four times the spatial resolution for some channels - and will collect pictures of the planet at a faster cadence than the 15-minute resolution of the old series. The ABI can also scan features of interest, such as hurricanes or thunderstorms, at 1-minute or even 30-second intervals. This high refresh rate will allow forecasters to observe storm structures that evolve too rapidly for legacy sensors to capture, but hold key information related to severe weather onset. All of these features mean forecasters will have more accurate information to use when making time-critical forecasts of weather events year-round. This will include severe storms and squall lines in the spring and summer months, tropical cyclones in the late summer and early fall, and powerful winter weather systems. Development of satellite-based products and forecaster-friendly tools has been at the core of research at the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University since its inception in 1980. One of only 16 cooperative partnerships with NOAA nationwide, CIRA harnesses the research excellence of CSU, particularly the Department of Atmospheric Science, to bridge the gap that often exists between basic research and operation applications. CIRA's expertise in satellite remote sensing and computer modeling of the Earth's atmosphere plays an important role in advancing NOAA's operational capabilities, for the benefit of all. As part of the nationwide GOES-R Proving Ground initiative, sponsored by NOAA, researchers at CIRA have developed several new forecast products to use the new features of GOES-R. They are in the process of deploying these products to NOAA regional centers and National Weather Service forecast offices nationwide. CIRA research scientist Steve Miller leads a team to harness the utility of the GOES-R Advanced Baseline Imager. They have developed a suite of algorithms that will maximize the potential of the ABI data to produce ultra-high-definition, true-color images of the planet. They've done this using data from a sister geostationary satellite launched by the Japan Meteorological Agency, which carries a similar instrument to the ABI. Miller's team's work will help characterize surface features such as wildfires, snowfields, dust storms, dense fog layers, and other difficult-to-identify phenomena. "Through its vastly improved combination of space, time, and multi-channel coverage, the ABI will give us an unprecedented ability to identify the unique 'fingerprints' of various surface and atmospheric features, allowing us to distinguish between them in what tend to be very complex scenes," Miller said. "One new capability we are all very excited about is true color imagery, which is perhaps the most visually intuitive form of satellite imagery and one that we can all relate to. It captures the wonder and beauty of our Blue Marble planet while at the same time giving forecasters a practical, at-a-glance tool for rapidly assessing the current weather situation." The lightning mapper instrument, called the GLM, will provide better forecast capabilities for both severe storms over land and tropical weather at sea, including enhanced identification of tropical storms as they rapidly intensify into powerful systems. The recent devastation caused by Hurricane Matthew as it roared through the Caribbean before hitting the U.S. eastern seaboard demonstrates the need for the best possible information for these storms. NOAA scientist John Knaff, one of the NOAA researchers embedded at CIRA, is developing tools to look at lightning frequency and intensity inside these storms using the GLM as a metric for storm strength. The more lightning that is occurring in these storms, the stronger the updrafts that feed the storm, and the more likely it will be that the storm strengthens. Improving weather forecasts over the continental U.S. is another area where NOAA researchers, working hand-in hand with CSU and CIRA, are making progress. By utilizing every channel the ABI has to offer, NOAA researcher Dan Lindsey leads a team at CIRA to seamlessly blend observations from GOES-R into a graphical representation that matches exactly the visualizations created by our most sophisticated weather forecast models. Weather forecasters will then see weather as it evolves over time, with the ability to match their view with the predicted changes in weather. If the forecast model has errors in it, the transition between observations and forecasts will be much more apparent, and the forecast guidance can be adjusted appropriately. CIRA researchers led by Bernie Connell are also putting together training programs for meteorologists nationwide to learn how best to use these products. Connell's work will help forecasters take full advantage of GOES-R capabilities starting on day one. Additionally, Andrea Schumacher of CIRA serves as a liaison between the GOES-R program and the National Hurricane Center, where she helps evaluate GOES-R products to optimize their use in improving hurricane forecasting. "Hurricanes spend the majority of their lifetimes over the open ocean, making geostationary satellite data a crucial data source for forecasters," Schumacher said. "The improvements and enhancements provided by GOES-R are going to give forecasters an unprecedented view of the tropical oceans, which is expected to improve their ability to monitor and predict these powerful storms." As the future of satellite technology becomes today's reality, researchers at CSU, in partnership with NOAA and NASA, will continue to lead the way in developing better and more accurate forecast products. More information about CIRA and its ongoing GOES-R research can be found at:

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