Geostationary Operational Environmental Satellite Series Research Articles

Calibration of the GOES 6–16 high-energy proton detectors based on modelling of ground level enhancement energy spectra

For several decades, the Geostationary Operational Environmental Satellites (GOES) series have provided both real-time and historical data for radiation exposure estimation and solar proton radiation environment modelling. Recently, several groups conducted calibration studies that significantly reduced the uncertainties on the response of GOES proton detectors, thus improving the reliability of the spectral observations of solar energetic particle events. In this work, the long-established Band function fitting set for past ground level enhancements (GLEs) and their recent revision are used as references to estimate the best matching energies of proton channels of GOES 6–16, with emphasis on comparing with previous calibration studies on the high energetic proton measurements. The calculated energies for different missions in the same series (GOES 8, 10, 11) show overall consistency but with small variations, and differences among missions of different series are noticeable for measurements crossing the past three solar cycles, though the results are sensitive to the method used to subtract background fluxes. The discrepancy and agreement with previous calibration efforts are demonstrated with other independent analyses. It is verified that the integral channel P11 of GOES 6–16 can be reliably used as a differential proton channel with an effective energy of about 1 GeV. Therefore, the multi-decade in situ measurements of the GOES series can be utilized with more extensive energy coverage to improve space radiation environment models.

A Statistical Model for Estimating Midday NDVI from the Geostationary Operational Environmental Satellite (GOES) 16 and 17

The newest version of the Geostationary Operational Environmental Satellite series (GOES-16 and GOES-17) includes a near infrared band that allows for the calculation of normalized difference vegetation index (NDVI) at a 1 km at nadir spatial resolution every five minutes throughout the continental United States and every ten minutes for much of the western hemisphere. The usefulness of individual NDVI observations is limited due to the noise that remains even after cloud masks and data quality flags are applied, as much of this noise is negatively biased due to scattering within the atmosphere. Fortunately, high temporal resolution NDVI allows for the identification of consistent diurnal patterns. Here, we present a novel statistical model that utilizes this pattern, by fitting double exponential curves to the diurnal NDVI data, to provide a daily estimate of NDVI over forests that is less sensitive to noise by accounting for both random observation errors and atmospheric scattering biases. We fit this statistical model to 350 days of observations for fifteen deciduous broadleaf sites in the United States and compared the method to several simpler potential methods. Of the days 60% had more than ten observations and were able to be modeled via our methodology. Of the modeled days 72% produced daily NDVI estimates with <0.1 wide 95% confidence intervals. Of the modeled days 13% were able to provide a confident NDVI value even if there were less than five observations between 10:00–14:00. This methodology provides estimates for daily midday NDVI values with robust uncertainty estimates, even in the face of biased errors and missing midday observations.

Naïve Bayesian Precipitation Type Retrieval from Satellite Using a Cloud-Top and Ground-Radar Matched Climatology

Abstract Satellite-based precipitation estimates are a vital resource for hydrologic applications in data-sparse regions of the world, particularly at daily or longer time scales. With the launch of a new generation of high-resolution imagers on geostationary platforms such as the Geostationary Operational Environmental Satellite series R (GOES-R), an opportunity exists to advance the detection and estimation of flash-flood-scale precipitation events from space beyond what is currently available. Because visible and infrared sensors can only observe cloud-top properties, many visible- and infrared-band-based rainfall algorithms attempt to first classify clouds before deriving a rain rate. This study uses a 2-yr database of cloud-top properties from proxy Advanced Baseline Imager radiances from GOES-R matched to surface precipitation types from the Multi-Radar Multi-Sensor (MRMS) system to develop a naïve Bayesian precipitation type classifier for the four major types of precipitation in MRMS: stratiform, convective, tropical, and hail. Evaluation of the naïve Bayesian precipitation type product showed a bias toward classifying convective and stratiform at the expense of tropical and hail. The tropical and hail classes in MRMS are derived based on the vertical structure and magnitude of radar reflectivity, which may not translate to an obvious signal at cloud top for a satellite-based algorithm. However, the satellite-based product correctly classified the hail areas as being convective in nature for the vast majority of missed hail events.

Improvements to the GOES-R Rainfall Rate Algorithm

Abstract The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar–gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%.

Interannual variation in biomass burning and fire seasonality derived from geostationary satellite data across the contiguous United States from 1995 to 2011

AbstractWildfires exhibit a strong seasonality that is driven by climatic factors and human activities. Although the fire seasonality is commonly determined using burned area and fire frequency, it could also be quantified using biomass consumption estimates that directly represent biomass loss (a combination of the area burned and the fuel loading). Therefore, in this study a data set of long‐term biomass consumed was derived from geostationary satellite data to explore the interannual variation in the fire seasonality and the possible impacts of climate change and land management practices across the Contiguous United States (CONUS). Specifically, daily biomass consumed data were derived using the fire radiative power retrieved from Geostationary Operational Environmental Satellites series with a pixel size of 4–10 km from 1995 to 2011. Annual fire seasonality metrics including the fire season duration, the timing of the start, peak, and end of the fire season, and interannual variation and trends were derived from the 17 year biomass consumed record. These metrics were associated with climatic factors to examine drivers and mediators of fire seasonality. The results indicate that biomass consumed significantly increased at a rate of 2.87 Tg/yr; however, the derived fire season duration exhibited a shortening trend in various states over the western CONUS and no significant trend in most other regions. This suggests that the frequency of extreme fire events has increased, which is perhaps associated with an observed increase of extreme weather conditions. Further, both the start and the end of the fire season exhibited an early shift (1.5–5 d/yr) in various eastern states although a late shift occurred in Arizona and Oregon. The interannual variation and trend in the fire seasonality was more strongly related to temperature in the western CONUS and to precipitation in the southeast. The Palmer Drought Severity Index was found to effectively reflect interannual variations in total biomass consumed although it was poorly correlated to the fire seasonality metrics. The results indicate that across the CONUS, the spatial patterns of the start, peak, and end of the fire season shift regularly in various regions in response to latitudinal gradients of temperature variation.

Effect of Different MODIS Emissivity Products on Land-Surface Temperature Retrieval From GOES Series

The National Oceanic and Atmospheric Administration's National Environmental Satellite, Data, and Information Service is developing an operational land-surface temperature (LST) product from the U.S. Geostationary Operational Environmental Satellite (GOES) series 13, 14, and 15, which makes use of the Moderate Resolution Imaging Spectroradiometer (MODIS) monthly emissivity. However, there is a latency problem since the MODIS monthly emissivity data are available at least a month late. In this study, we investigated using alternative emissivity data sets, including the ten-year monthly average emissivity, the last month emissivity, and the same month emissivity in the last year. We also tested current monthly emissivity and current weekly emissivity for comparison and evaluation. The study area is in the continental United States (25°N-50°N and 125°W-65°W), and the temporal frame is April, July, October, and December, which represents the four seasons in a year. Based on the modified dual-window algorithm, LST is derived and validated against the SURFace RADiation (SURFRAD) budget network ground observations. The results show that the ten-year monthly average emissivity performs best by retrieving stable and accurate LST.

Preliminary Development and Evaluation of Lightning Jump Algorithms for the Real-Time Detection of Severe Weather

Abstract Previous studies have demonstrated that rapid increases in total lightning activity (intracloud + cloud-to-ground) are often observed tens of minutes in advance of the occurrence of severe weather at the ground. These rapid increases in lightning activity have been termed “lightning jumps.” Herein, the authors document a positive correlation between lightning jumps and the manifestation of severe weather in thunderstorms occurring across the Tennessee Valley and Washington D.C. A total of 107 thunderstorms from the Tennessee Valley; Washington, D.C.; Dallas, Texas; and Houston, Texas, were examined in this study. Of the 107 thunderstorms, 69 thunderstorms fall into the category of nonsevere and 38 into the category of severe. From the dataset of 69 isolated nonsevere thunderstorms, an average, peak, 1-min flash rate of 10 flashes per minute was determined. A variety of severe thunderstorm types were examined for this study, including a mesoscale convective system, mesoscale convective vortex, tornadic outer rainbands of tropical remnants, supercells, and pulse severe thunderstorms. Of the 107 thunderstorms, 85 thunderstorms (47 nonsevere, 38 severe) were from the Tennessee Valley and Washington, D.C., and these 85 thunderstorms tested six lightning jump algorithm configurations (Gatlin, Gatlin 45, 2σ, 3σ, Threshold 10, and Threshold 8). Performance metrics for each algorithm were then calculated, yielding encouraging results from the limited sample of 85 thunderstorms. The 2σ lightning jump algorithm had a high probability of detection (POD; 87%), a modest false-alarm rate (FAR; 33%), and a solid Heidke skill score (0.75). These statistics exceed current NWS warning statistics with this dataset; however, this algorithm needs further testing because there is a large difference in sample sizes. A second and more simplistic lightning jump algorithm named the Threshold 8 lightning jump algorithm also shows promise, with a POD of 81% and a FAR of 41%. Average lead times to severe weather occurrence for these two algorithms were 23 min. The overall goal of this study is to advance the development of an operationally applicable jump algorithm that can be used with either total lightning observations made from the ground, or in the near future from space using the Geostationary Operational Environmental Satellite Series R (GOES-R) Geostationary Lightning Mapper.

Retrieval of Total Column Ozone From Imagers Onboard Geostationary Satellites

Geostationary imagers such as the Advanced Baseline Imager (ABI) proposed for the next generation of Geostationary Operational Environmental Satellites (GOESs), i.e., GOES Series R and beyond, and the spinning enhanced visible and InfraRed Imager (SEVIRI) onboard METEOSAT 8 provide atmospheric total column ozone (TCO) with high temporal (better than 15 min) and spatial (better than 5 km) resolutions. The purpose of this paper is to present a method that evolved from the current GOES sounder TCO retrieval that can be applied to ABI, with SEVIRI serving as a proxy for ABI. Although ABI and SEVIRI have fewer infrared spectral bands than the current series of GOES (-8 to -P) sounders, ABI and SEVIRI can provide TCO with an accuracy that is comparable to the current GOES sounder by using forecast temperature profiles as additional a priori information. Despite the need for additional data, the greatest advantage of ABI and SEVIRI is their fast 15-min coverage of the full disk. The SEVIRI TCO demonstrates how well geostationary imagers can capture ozone transport and change at high temporal and spatial resolutions. The estimated TCO has very good agreement (R = 0.92 and root-mean-square error = 3.7%) with the total ozone measurements from the Ozone Monitoring Instrument (OMI) onboard the Earth Observing System aura platform.

Calibrating a solar x-ray telescope using the Crab Nebula

The Crab Nebula (see Figure 1) has been used to calibrate xray detectors on many satellite-, rocket-, and balloon-borne missions. It provides a bright and stable broadband source with a well-characterized spectrum. For space-based telescopes, in particular, this allows adjusting to changes over time in filters, electronics, optics, and other sensors. We demonstrate here the feasibility of extending this calibration practice to the Solar XRay Imager (SXI-N) telescope aboard a Geostationary Operational Environmental Satellites (GOES) mission launched in May 2006. The primary mission of the GOES series, which is operated by the National Oceanic and Atmospheric Administration (NOAA), is to monitor weather on Earth. SXI’s purpose is to provide real-time images of the sun and solar corona between 6 and 60A, which helps space weather forecasters by providing longterm solar climatological observations. Previous SXI instruments were not calibrated. They suffer from ongoing filter and electronic unit degradation, and a consequent degree of measurement uncertainty. Onboard x-ray calibration of satellite instruments is not generally practical because the required sources would be too large. NOAA’s current plan for calibrating SXI-N would use expensive sounding-rocket underflights to make solar observations simultaneously with the GOES. In-flight x-ray calibrations using the Crab Nebula may not have been considered in these cases because of the nebula’s low flux compared with that of the solar phenomena being measured. To evaluate the feasibility of this approach, we used existing Crab Nebula measurements from the Einstein Observatory, shown in Figure 2, to see whether SXI-N observations could achieve a signal-to-noise ratio (SNR) of at least 5, which would meet the instrument’s 20% photometric requirement. We also modeled likely noise sources that would enter into our signalto-noise calculations. Figure 1. The Crab Nebula, shown at visible wavelengths. Courtesy of NASA and ESA (Hubble WFPC2), and Allison Loll and Jeff Hester (Arizona State University).

Model for solar proton risk assessment

A statistical model for cumulative solar proton event fluences during space missions is presented that covers both the solar minimum and solar maximum phases of the solar cycle. It is based on data from the Interplanetary Monitoring Platform and Geostationary Operational Environmental Satellites series of satellites, which are integrated together to allow the best features of each data set to be used to the best advantage. This allows the fluence-energy spectra to be extended out to energies of 327 MeV.

Principal Component Image Analysis of MODIS for Volcanic Ash. Part I: Most Important Bands and Implications for Future GOES Imagers

In Part I of this paper, the infrared bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) are analyzed for volcanic ash signals using principal component image analysis. Target volcanoes included Popocatepetl volcano near Mexico City and Cleveland volcano in the Aleutian Islands. The analyses were performed to determine the MODIS bands that contribute the most to detecting volcanic ash. Even though the explained variance and signal-to-noise ratio of most of these new images are generally small, several of them provide views of volcanic ash with good contrast to the image background and other image features. Both day and night examples indicate that volcanic ash can be readily detected by combinations of MODIS bands to determine not only the ash extent but also qualitative variations in the concentration and height of the ash. The 36 bands on MODIS give much more flexibility for ash detection than the 4 bands on the current Geostationary Operational Environmental Satellite (GOES) Imager. In particular, MODIS bands 28–32, in the water vapor and longwave infrared portions of the spectrum, contributed most frequently to the detection of airborne volcanic ash. These include bands 28–30 in the 7.3–9.7-μm portion of the spectrum known for volcanic signals. Several of the MODIS bands that proved useful are bands projected for inclusion in the next major upgrade to the GOES Imager (scheduled for 2012). However, band 30 (9.7 μm) is neither available on the current GOES series nor planned for future GOES Imagers. In Part II of this paper, MODIS data for the same volcanic cases examined in Part I are used for specific simulations of current and near-term GOES imagery. The purpose of the simulations is to assess the impact of changes that will occur in the spectral bands of the GOES-M Imager (launched in 2001 and renamed GOES-12) when it becomes operational.