Search Results

You are looking at 1 - 10 of 10 items for

  • Author or Editor: Jaime Daniels x
  • Refine by Access: All Content x
Clear All Modify Search
Gary P. Ellrod, Rao V. Achutuni, Jaime M. Daniels, Elaine M. Prins, and James P. Nelson III

The Geostationary Operational Environmental Satellite-8 (GOES-8), the first in the GOES I–M series of advanced meteorological satellites was launched in April 1994 and became operational at 75 °W longitude the following year. GOES-8 features numerous improvements over prior GOES platforms such as 1) improved resolution in the infrared (IR) and water vapor bands, 2) reduced instrument noise, 3) 10-bit visible and IR digitization, 4) greater image frequency, 5) more spectral bands, and 6) an independent sounder. A qualitative and quantitative comparison of the imager data from GOES-8 and GOES-7 shows that imagery from the newer spacecraft is superior in most respects. Improvements in resolution and instrument noise on GOES-8 provide sharper, cleaner images that allow easier detection of significant meteorological or oceanographic features. Infrared temperature comparisons between GOES-8 and GOES-7 were within 0.5°–2.0°C for all IR bands, indicating consistency between the two spacecraft. Visible band albedos from GOES-8 were at least 50% greater than GOES-7 for a wide range of scenes, suggesting that output from the GOES-7 visible detectors had degraded since its launch in 1987. Products derived from GOES-8 imager data for observing fog at night, fire detection, heavy precipitation estimation, and upper-level winds based on cloud or water vapor motion have been shown to be superior to similar products from GOES-7. Early difficulties with image registration and IR striping were alleviated after the first year. Based on the performance of GOES-8, future spacecraft in the GOES I–M series can be expected to provide many years of useful service to meteorologists, oceanographers, and the environmental monitoring community.

Full access
Wayne C. Bresky, Jaime M. Daniels, Andrew A. Bailey, and Steven T. Wanzong

Abstract

Comparisons between satellite-derived winds and collocated rawinsonde observations often show a pronounced slow speed bias at mid- and upper levels of the atmosphere. A leading cause of the slow speed bias is the improper assignment of the tracer to a height that is too high in the atmosphere. Height errors alone cannot fully explain the slow bias, however. Another factor influencing the speed bias is the size of the target window used in the tracking step. Tracking with a large target window can cause excessive averaging to occur and a smoothing of the instantaneous wind field. Conversely, if too small a window is specified, there is an increased risk of finding a false match. The authors have developed a new “nested tracking” approach that isolates the dominant local motion within a cloud scene and minimizes the smoothing of the motion estimate. A major advantage of the new approach is the ability to identify which pixels within the cloud scene are contributing to the tracking solution. Knowing which pixels contribute to the dominant motion allows for a more representative height to be derived, thereby directly linking the height assignment to the tracking process, which is an important goal for producers of global atmospheric motion vector (AMV) data. When compared with equivalent rawinsondes, the AMVs derived with the new approach show a considerable improvement in the speed bias and root-mean-square error over a control set of AMVs derived with more-conventional methods.

Full access
Christopher Velden, William E. Lewis, Wayne Bresky, David Stettner, Jaime Daniels, and Steven Wanzong

Abstract

It is well known that global numerical model analyses and forecasts benefit from the routine assimilation of atmospheric motion vectors (AMVs) derived from meteorological satellites. Recent studies have also shown that the assimilation of enhanced (spatial and temporal) AMVs can benefit research-mode regional model forecasts of tropical cyclone track and intensity. In this study, the impact of direct assimilation of enhanced (higher resolution) AMV datasets in the NCEP operational Hurricane Weather Research and Forecasting Model (HWRF) system is investigated. Forecasts of Atlantic tropical cyclone track and intensity are examined for impact by inclusion of enhanced AMVs via direct data assimilation. Experiments are conducted for AMVs derived using two methodologies (“HERITAGE” and “GOES-R”), and also for varying levels of quality control in order to assess and inform the optimization of the AMV assimilation process. Results are presented for three selected Atlantic tropical cyclone events and compared to Control forecasts without the enhanced AMVs as well as the corresponding operational HWRF forecasts. The findings indicate that the direct assimilation of high-resolution AMVs has an overall modest positive impact on HWRF forecasts, but the impact magnitudes are dependent on the 1) availability of rapid scan imagery used to produce the AMVs, 2) AMV derivation approach, 3) level of quality control employed in the assimilation, and 4) vortex initialization procedure (including the degree to which unbalanced states are allowed to enter the model analyses).

Full access
John F. Weaver, John A. Knaff, Dan Bikos, Gary S. Wade, and Jaime M. Daniels
Full access
John F. Weaver, John A. Knaff, Dan Bikos, Gary S. Wade, and Jaime M. Daniels

Abstract

This paper utilizes a severe thunderstorm case from 24 July 2000 to demonstrate the relevance of Geostationary Operational Environmental Satellite (GOES) rapid-scan imagery and sounder data in the short-range forecasting and nowcasting time frames. Results show how these data can be employed quickly and effectively during the warning decision-making process. Various aspects of the severe storm environment are identified that could only be diagnosed in this case using satellite data.

The data used in this study are unique in that the imager and sounder input both come from one of the newest of the geostationary satellites, GOES-11. The datasets were collected as a part of the satellite's 6-week science test. During this test period, continuous 1-min imagery and 30-min sounder data were available. The new satellite has now been placed on standby and will be put in service when either GOES-East or GOES-West fails.

Two new high-resolution satellite products are presented that are currently in the developmental phase. These will be field tested and implemented within the next couple of years.

Full access
Timothy J. Schmit, Paul Griffith, Mathew M. Gunshor, Jaime M. Daniels, Steven J. Goodman, and William J. Lebair

Abstract

The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) is America’s next-generation geostationary advanced imager. GOES-R launched on 19 November 2016. The ABI is a state-of-the-art 16-band radiometer, with spectral bands covering the visible, near-infrared, and infrared portions of the electromagnetic spectrum. Many attributes of the ABI—such as spectral, spatial, and temporal resolution; radiometrics; and image navigation/registration—are much improved from the current series of GOES imagers. This paper highlights and discusses the expected improvements of each of these attributes. From ABI data many higher-level-derived products can be generated and used in a large number of environmental applications. The ABI’s design allows rapid-scan and contiguous U.S. imaging automatically interleaved with full-disk scanning. In this paper the expected instrument attributes are covered, as they relate to signal-to-noise ratio, image navigation and registration, the various ABI scan modes, and other parameters. There will be several methods for users to acquire GOES-R imagery and products depending on their needs. These include direct reception of the imagery via the satellite downlink and an online-accessible archive. The information from the ABI on the GOES-R series will be used for many applications related to severe weather, tropical cyclones and hurricanes, aviation, natural hazards, the atmosphere, the ocean, and the cryosphere.

The ABI on the GOES-R series is America’s next-generation geostationary advanced imager and will dramatically improve the monitoring of many phenomena at finer time and space scales.

Full access
Agnes H. N. Lim, James A. Jung, Sharon E. Nebuda, Jaime M. Daniels, Wayne Bresky, Mingjing Tong, and Vijay Tallapragada

Abstract

The assimilation of atmospheric motion vectors (AMVs) provides important wind information to conventional data-lacking oceanic regions, where tropical cyclones spend most of their lifetimes. Three new AMV types, shortwave infrared (SWIR), clear-air water vapor (CAWV), and visible (VIS), are produced hourly by NOAA/NESDIS and are assimilated in operational NWP systems. The new AMV data types are added to the hourly infrared (IR) and cloud-top water vapor (CTWV) AMV data in the 2016 operational version of the HWRF Model. In this study, we update existing quality control (QC) procedures and add new procedures specific to tropical cyclone assimilation. We assess the impact of the three new AMV types on tropical cyclone forecasts by conducting assimilation experiments for 25 Atlantic tropical cyclones from the 2015 and 2016 hurricane seasons. Forecasts are analyzed by considering all tropical cyclones as a group and classifying them into strong/weak storm vortices based on their initial model intensity. Metrics such as track error, intensity error, minimum central pressure error, and storm size are used to assess the data impact from the addition of the three new AMV types. Positive impact is obtained for these metrics, indicating that assimilating SWIR-, CAWV-, and VIS-type AMVs are beneficial for tropical cyclone forecasting. Given the results presented here, the new AMV types were accepted into NOAA/NCEP’s operational HWRF for the 2017 hurricane season.

Full access
Steven J. Nieman, W. Paul Menzei, Christopher M. Hayden, Donald Gray, Steven T. Wanzong, Christopher S. Velden, and Jaime Daniels

Cloud-drift winds have been produced from geostationary satellite data in the Western Hemisphere since the early 1970s. During the early years, winds were used as an aid for the short-term forecaster in an era when numerical forecasts were often of questionable quality, especially over oceanic regions. Increased computing resources over the last two decades have led to significant advances in the performance of numerical forecast models. As a result, continental forecasts now stand to gain little from the inspection or assimilation of cloud-drift wind fields. However, the oceanic data void remains, and although numerical forecasts in such areas have improved, they still suffer from a lack of in situ observations. During the same two decades, the quality of geostationary satellite data has improved considerably, and the cloud-drift wind production process has also benefited from increased computing power. As a result, fully automated wind production is now possible, yielding cloud-drift winds whose quality and quantity is sufficient to add useful information to numerical model forecasts in oceanic and coastal regions. This article will detail the automated cloud-drift wind production process, as operated by the National Environmental Satellite Data and Information Service within the National Oceanic and Atmospheric Administration.

Full access
Christopher Velden, Jaime Daniels, David Stettner, David Santek, Jeff Key, Jason Dunion, Kenneth Holmlund, Gail Dengel, Wayne Bresky, and Paul Menzel

The evolving constellation of environmental/meteorological satellites and their associated sensor technology is rapidly advancing. This is providing opportunities for creatively improving satellite-derived products used in weather analysis and forecasting. For example, the retrieval methods for deriving atmospheric motion vectors (AMVs) from satellites have been expanding and evolving since the early 1970s. Contemporary AMV processing methods are continuously being updated and advanced through the exploitation of new sensor technologies and innovative new approaches. It is incumbent upon the research community working in AMV extraction techniques to ensure that the quality of the current operational products meets or exceeds the needs of the user community. In particular, the advances in data assimilation and numerical weather prediction in recent years have placed an increasing demand on data quality.

To keep pace with these demands, innovative research toward improving methods of deriving winds from satellites has been a focus of the World Meteorological Organization and Coordination Group for Meteorological Satellites (CGMS) cosponsored International Winds Workshops (IWWs). The IWWs are held every 2 yr, and bring together AMV researchers from around the world to present new ideas on AMV extraction techniques, interpretation, and applications. The NWP community is always well represented at these workshops, which provide an important exchange of information on the latest in data assimilation issues. This article draws from recent IWWs, and describes several new advances in satellite-produced wind technologies, derivation methodologies, and products. Examples include AMVs derived from Geostationary Operational Environmental Satellite (GOES) rapid scans and the shortwave IR channel, AMVs over the polar regions from the Moderate Resolution Imaging Spectroradiometer (MODIS), improved AMV products from the new Meteosat Second Generation satellite, and new processing approaches for deriving AMVs. The article also provides a glimpse into the pending opportunities that will be afforded with emerging/anticipated new sensor technologies.

Full access
Elaine M. Prins, Christopher S. Velden, Jeffrey D. Hawkins, F. Joseph Turk, Jaime M. Daniels, Gerald J. Dittberner, Kenneth Holmlund, Robbie E. Hood, Arlene G. Laing, Shaima L. Nasiri, Jeffery J. Puschell, J. Marshall Shepherd, and John V. Zapotocny
Full access