Jaime Daniels

Recent Innovations in Deriving Tropospheric Winds from Meteorological Satellites

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 sat...

Fully Automated Cloud-Drift Winds in NESDIS Operations

Abstract 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 whos...

A Closer Look at the ABI on the GOES-R Series

AbstractThe 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 regis...

New Methods toward Minimizing the Slow Speed Bias Associated with Atmospheric Motion Vectors

AbstractComparisons 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 ...

An Assessment of GOES-8 Imager Data Quality

Abstract 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...

Satellite Observations of a Severe Supercell Thunderstorm on 24 July 2000 Made during the GOES-11 Science Test

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 satellites 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 dev...

Assimilation of High-Resolution Satellite-Derived Atmospheric Motion Vectors: Impact on HWRF Forecasts of Tropical Cyclone Track and Intensity

AbstractIt 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 comp...

Latest assessment of GOES-R (16) Advanced Baseline Imager (ABI) data quality from an application and training perspective

The GOES-R series is four spacecraft with six instruments that comprise the next generation of U.S. geostationary sensors. GOES-R launched on November 19, 2016 and became GOES-16 on November 29, when it reached geostationary orbit. The primary instrument, the earth-viewing imager on the series, is the Advanced Baseline Imager (ABI). The ABI is a significant improvement over the current GOES imager. The number of spectral bands increases by a factor of more than three, the spatial resolution increases by a factor of four and the scanning speed increases by a factor of five. The presentation will include both first light imagery and imagery from the post launch test period.

Near Real-Time processing at ASSISTT: AHI Winds and JPSS Risk Reduction Project products

The Algorithm Scientific Software Integration and System Transition Team (ASSISTT) at NESDIS STAR (National Environmental Satellite Data and Information Service, Center for Satellite Applications and Research) has designed, developed and implemented a Near Real-Time (NRT) processing system to process algorithms for scientists and other stakeholders. The system generates products using the STAR Algorithm Processing Framework (SAPF). The SAPF was originally developed by ASSISTT as a testbed for GOES-R project products and have expanded to the other projects such as VIIRS Polar Winds, GOES Winds, AHI Winds and JPSS Risk Reduction Project. The NRT processing system specific to AHI Winds and JPSS Risk Reduction Project products will be introduced here.

GOES-R Algorithm Working Group (AWG)

For the next-generation of GOES-R instruments to meet stated performance requirements, state-of-the-art algorithms will be needed to convert raw instrument data to calibrated radiances and derived geophysical parameters (atmosphere, land, ocean, and space weather). The GOES-R Program Office (GPO) assigned the NOAA/NESDIS Center for Satellite Research and Applications (STAR) the responsibility for technical leadership and management of GOES-R algorithm development and calibration/validation. STAR responded with the creation of the GOES-R Algorithm Working Group (AWG) to manage and coordinate development and calibration/validation activities for GOES-R proxy data and geophysical product algorithms. The AWG consists of 15 application teams that bring expertise in product algorithms that span atmospheric, land, oceanic, and space weather disciplines. Each AWG teams will develop new scientific Level- 2 algorithms for GOES-R and will also leverage science developments from other communities (other government agencies, universities and industry), and heritage approaches from current operational GOES and POES product systems. All algorithms will be demonstrated and validated in a scalable operational demonstration environment. All software developed by the AWG will adhere to new standards established within NOAA/NESDIS. The AWG Algorithm Integration Team (AIT) has the responsibility for establishing the system framework, integrating the product software from each team into this framework, enforcing the established software development standards, and preparing system deliveries. The AWG will deliver an Algorithm Theoretical Basis Document (ATBD) for each GOES-R geophysical product as well as Delivered Algorithm Packages (DAPs) to the GPO.