James P. Nelson

Validation and use of GOES sounder moisture information

Abstract The Geostationary Operational Environmental Satellite (GOES) sounders have provided quality hourly radiances and derived products over the continental United States and adjacent oceans for more than five years. The products derived from the radiances include temperature and moisture profiles; total precipitable water vapor (TPW); atmospheric stability indices, such as convective available potential energy (CAPE) and lifted index (LI); cloud-top properties; total column ozone; and midlevel motion. This paper focuses on validation and use of moisture profiles derived in clear regions. Validations are made with respect to collocated radiosondes, a microwave radiometer, and parallel runs of the regional Eta Model system. The ground-based microwave radiometer enables comparisons throughout the day, instead of only at conventional radiosonde launch times (0000 and 1200 UTC). The validations show that the sounder products provide unique information about the state of the atmosphere. The GOES sounder moi...

A Case Study of the Sensitivity of the Eta Data Assimilation System

Abstract A case study is utilized to determine the sensitivity of the Eta Data Assimilation System (EDAS) to all operational observational data types used within it. The work described in this paper should be of interest to Eta Model users trying to identify the impact of each data type and could benefit other modelers trying to use EDAS analyses and forecasts as initial conditions for other models. The case study chosen is one characterized by strong Atlantic and Pacific maritime cyclogenesis, and is shortly after the EDAS began using three-dimensional variational analysis. The control run of the EDAS utilizes all 34 of the operational data types. One of these data types is then denied for each of the subsequent experimental runs. Differences between the experimental and control runs are analyzed to demonstrate the sensitivity of the EDAS system to each data type for the analysis and subsequent 48-h forecasts. Results show the necessity of various nonconventional observation types, such as aircraft data,...

An Impact Study of Five Remotely Sensed and Five In Situ Data Types in the Eta Data Assimilation System

Abstract The impact of 10 data types used in the Eta Data Assimilation/Forecast System (EDAS) is studied for extended-length time periods during three seasons. Five of the data types are remotely sensed satellite data, and the other five are in situ. The satellite data types include three-layer and vertically integrated precipitable water, temperature data down to cloud top, infrared cloud-drift winds, and water vapor cloud-top winds. The five in situ data types consist of two rawinsonde and two aircraft observation types along with surface land observations. The work described in this paper is relevant for Eta Model users trying to identify the impact of remotely sensed, largely maritime data types and in situ, largely land-based data types. The case studies chosen consist of 11-day periods during December 1998, April 1999, and July 1999. During these periods, 11 EDAS runs were executed twice daily. The 11 runs include a control run, which utilizes all data types used in the EDAS, and 10 experimental run...

A Four-Season Impact Study of Rawinsonde, GOES, and POES Data in the Eta Data Assimilation System. Part II: Contribution of the Components

Abstract The impact of in situ rawinsonde observations (raob), remotely sensed Geostationary Operational Environmental Satellite (GOES), and Polar-Orbiting Operational Environmental Satellite (POES) observations routinely used in NCEP’s Eta Data Assimilation/Forecast System (EDAS) is studied for extended-length time periods during four seasons. This work examines the contribution of nine individual components of the total observing system. The nine data types examined include rawinsonde mass and wind observations, GOES mass and wind observations, POES observations from the Microwave Sounding Unit (MSU), the Advanced Microwave Sounding Unit (AMSU-A and AMSU-B), the High Resolution Infrared Radiation Sounder (HIRS), and column total precipitable water and low-level wind observations from the Special Sensor Microwave Imager (SSM/I). The results are relevant for users of the Eta Model trying to compare/contrast the overall forecast impact of traditional, largely land-based rawinsonde observations against remo...

Estimation of Total Atmospheric Ozone from GOES Sounder Radiances with High Temporal Resolution

Abstract The potential for using Geostationary Operational Environmental Satellite (GOES) Sounder radiance measurements to monitor total atmospheric ozone is examined. A statistical regression using GOES Sounder spectral bands 1–15 radiances allows estimation of total atmospheric ozone. Hourly GOES ozone products have been generated since May 1998. GOES ozone estimates are compared with Total Ozone Mapping Spectrometer (TOMS) ozone measurements from the Earth Probe satellite and ground-based Dobson spectrometer ozone observations. Results show that the percentage root-mean-square (rms) difference between instantaneous TOMS and GOES ozone estimates ranges from 4% to 7%. Also, daily comparisons for 1998 between GOES ozone values and ground-based observations at Bismarck, North Dakota; Wallops Island, Virginia; and Nashville, Tennessee, show that the rms difference is approximately 21 Dobson units. Given the hourly measurements and high-spatial density provided by the GOES Sounder, GOES ozone estimates and a...

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...

A four-season impact study of rawinsonde, GOES, and POES Data in the Eta Data Assimilation System. Part I: The total contribution

Abstract The impact of in situ rawinsonde (raob) data, remotely sensed Geostationary Operational Environmental Satellite (GOES), and Polar Operational Environmental Satellite (POES) data routinely used in NCEP’s Eta Data Assimilation/Forecast System (EDAS) is studied for extended-length time periods during four seasons. The work described in this paper is relevant for users of the Eta Model trying to compare and contrast the overall forecast impact of traditional, mostly land-based rawinsonde data with remotely sensed data that are available domainwide. The case studies chosen consist of 15-day periods during fall 2001, winter 2001/02, spring 2002, and summer 2002. During these periods, a 32-km/60-layer November 2001 version of the EDAS is run four times at both 0000 and 1200 UTC. The four runs include a control run, which utilizes all data types routinely used in the EDAS, and three experimental runs in which either all rawinsonde, GOES, or POES data are denied. Differences between the experimental and c...

Real-Time Simulation of the GOES-R ABI for User Readiness and Product Evaluation

AbstractIn support of the Geostationary Operational Environmental Satellite R series (GOES-R) program, the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin–Madison is generating high quality simulated Advanced Baseline Imager (ABI) radiances and derived products in real time over the continental United States. These data are mainly used for testing data-handling systems, evaluating ABI-derived products, and providing training material for forecasters participating in GOES-R Proving Ground test bed activities. The modeling system used to generate these datasets consists of advanced regional and global numerical weather prediction models in addition to state-of-the-art radiative transfer models, retrieval algorithms, and land surface datasets. The system and its generated products are evaluated for the 2014 Pacific Northwest wildfires; the 2013 Moore, Oklahoma, tornado; and Hurricane Sandy. Simulated aerosol optical depth over the Front Range of Colorado duri...

Experimental GOES Sounder Products for the Assessment of Downburst Potential

Abstract Several experimental products derived from Geostationary Operational Environmental Satellite (GOES) Sounder retrievals (vertical profiles of temperature and moisture) have been developed to assist weather forecasters in assessing the potential for convective downbursts. The product suite currently includes the wind index (WINDEX), a dry microburst index, and the maximum difference in equivalent potential temperature (θe) from the surface to 300 hPa. The products are displayed as color-coded boxes or numerical values, superimposed on GOES visible, infrared, or water vapor imagery, and are available hourly, day and night, via the Internet. After two full summers of evaluation, the products have been shown to be useful in the assessment of atmospheric conditions that may lead to strong, gusty surface winds from thunderstorms. Two case studies are presented: 1) a severe downburst storm in southern Arizona that produced historic surface wind speeds and damage, and 2) multiple dry and wet downbursts in...

Geostationary Operational Environmental Satellite Imager infrared channel-to-channel co-registration characterization algorithm and its implementation in the ground system

Abstract Channel-to-channel co-registration is an important performance metric for the Geostationary Operational Environmental Satellite (GOES) Imager, and large co-registration errors can have a significant impact on the reliability of derived products that rely on combinations of multiple infrared (IR) channels. Affected products include the cloud mask, fog and fire detection. This is especially the case for GOES-13, in which the co-registration error between channels 2 ( 3.9     μ m ) and 4 ( 10.7     μ m ) can be as large as 1 pixel (or ∼ 4     km ) in the east-west direction. The GOES Imager IR channel-to-channel co-registration characterization (GII4C) algorithm is presented, which allows a systematic calculation of the co-registration error between GOES IR channel image pairs. The procedure for determining the co-registration error as a function of time is presented. The algorithm characterizes the co-registration error between corresponding images from two channels by spatially transforming one image using the fast Fourier transformation resampling algorithm and determining the distance of the transformation that yields the maximum correlation in brightness temperature. The GII4C algorithm is an area-based approach which does not depend on a fixed set of control points that may be impacted by the presence of clouds. In fact, clouds are a feature that enhances the correlations. The results presented show very large correlations over the majority of Earth-viewing pixels, with stable algorithm results. Verification of the algorithm output is discussed, and a global spatial-spectral gradient asymmetry parameter is defined. The results show that the spatial-spectral gradient asymmetry is strongly correlated to the co-registration error and can be an effective global metric for the quality of the channel-to-channel co-registration characterization algorithm. Implementation of the algorithm in the GOES ground system is presented. This includes an offline component to determine the time dependence of the co-registration errors and a real-time component to correct the co-registration errors based on the inputs from the offline component.