GOES-R Advanced Baseline Imager Research Articles

GOES-R Time Series for Early Detection of Wildfires with Deep GRU-Network

Early detection of wildfires has been limited using the sun-synchronous orbit satellites due to their low temporal resolution and wildfires’ fast spread in the early stage. NOAA’s geostationary weather satellites GOES-R Advanced Baseline Imager (ABI) can acquire images every 15 min at 2 km spatial resolution, and have been used for early fire detection. However, advanced processing algorithms are needed to provide timely and reliable detection of wildfires. In this research, a deep learning framework, based on Gated Recurrent Units (GRU), is proposed to detect wildfires at early stage using GOES-R dense time series data. GRU model maintains good performance on temporal modelling while keep a simple architecture, makes it suitable to efficiently process time-series data. 36 different wildfires in North and South America under the coverage of GOES-R satellites are selected to assess the effectiveness of the GRU method. The detection times based on GOES-R are compared with VIIRS active fire products at 375 m resolution in NASA’s Fire Information for Resource Management System (FIRMS). The results show that GRU-based GOES-R detections of the wildfires are earlier than that of the VIIRS active fire products in most of the study areas. Also, results from proposed method offer more precise location on the active fire at early stage than GOES-R Active Fire Product in mid-latitude and low-latitude regions.

Using GOES-R ABI Full-Disk Reflectance as a Calibration Source for the GOES Imager Visible Channels

The availability of onboard calibration for solar reflectance channels on recently launched advanced geostationary imagers provides an opportunity to revisit the calibration of the visible channels on past geostationary imagers, which lacked onboard calibration systems. This study used the data from the Advanced Baseline Imager (ABI) on GOES-16 and GOES-17 to calibrate the visible channels on the GOES-IP (GOES-8, -9, -10, -11, -12, -13, and -15) sensors (1994–2021). The visible channels are dominant sources of information for many of the essential climate variables from these sensors. The technique developed uses the stability of the integrated full-disk reflectance to define a calibration target that is applied to past sensors to generate new calibration equations. These equations are found to be stable and agree well with other established techniques. Given the lack of assumptions and ease of application, this technique offers a new calibration method that can be used to complement existing techniques used by the operational space agencies with the GSICS Project. In addition, its simplicity allows for its application to data that existed prior to many of the reference data employed in current calibration methods.

ProbSevere LightningCast: A Deep-Learning Model for Satellite-Based Lightning Nowcasting

Abstract Lightning strikes pose a hazard to human life and property, and can be difficult to forecast in a timely manner. In this study, a satellite-based machine learning model was developed to provide objective, short-term, location-specific probabilistic guidance for next-hour lightning activity. Using a convolutional neural network architecture designed for semantic segmentation, the model was trained using GOES-16 visible, shortwave infrared, and longwave infrared bands from the Advanced Baseline Imager (ABI). Next-hour GOES-16 Geostationary Lightning Mapper data were used as the truth or target data. The model, known as LightningCast, was trained over the GOES-16 ABI contiguous United States (CONUS) domain. However, the model is shown to generalize to GOES-16 full disk regions that are outside of the CONUS. LightningCast provides predictions for developing and advecting storms, regardless of solar illumination and meteorological conditions. LightningCast, which frequently provides 20 min or more of lead time to new lightning activity, learned salient features consistent with the scientific understanding of the relationships between lightning and satellite imagery interpretation. We also demonstrate that despite being trained on data from a single geostationary satellite domain (GOES-East), the model can be applied to other satellites (e.g., GOES-West) with comparable specifications and without substantial degradation in performance. LightningCast objectively transforms large volumes of satellite imagery into objective, actionable information. Potential application areas are also highlighted. Significance Statement The outcome of this research is a model that spatially forecasts lightning occurrence in a 0–60-min time window, using only images of clouds from the GOES-R Advanced Baseline Imager. This model has the potential to provide early alerts for developing and approaching hazardous conditions.

On the Potential Optical Signature of Convective Turbulence over the West Florida Shelf

Atmospheric cold front propagation across the northern Gulf of Mexico is characterized by elevated surface wind velocities and a ~10–15 °C drop in surface air temperatures. These meteorological conditions result in significant heat energy losses from the surface ocean to the overlying atmosphere. These seasonally recurring cold-air outbreak events may penetrate the southern portion of the West Florida continental shelf and initiate turbulent and convective overturn of the water column. Examination of true color images derived from ocean-viewing, satellite-based radiometer data reveals coincident and substantial surface water discolorations that are optically similar to smaller-scale “whiting events,” despite the regional-scale extent of the observed phenomenon (>25,000 km2). Coupled air–sea numerical simulations suggest the surface water discoloration occurs and is sustained where the entire water column is dynamically unstable. The simulation results indicate significant density (σt) inversions between the surface and bottom waters. Thus, the combined numerical model and remote sensing analysis suggest that convective turbulence may be contributing to the sustained ventilation of bottom waters containing a high concentration of suspended particulates. High-temporal resolution true color images rendered from the GOES-R Advanced Baseline Imager (ABI) data appear to support the surface water discoloration’s turbulent-driven nature.

Assimilation of Synthetic GOES-R ABI Infrared Brightness Temperatures and WSR-88D Radar Observations in a High-Resolution OSSE

This study uses an observing system simulation experiment to explore the impact of assimilating GOES-R Advanced Baseline Imager (ABI) 6.95-μm brightness temperatures and Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity observations in an ensemble data assimilation system. A high-resolution truth simulation was used to create synthetic radar and satellite observations of a severe weather event that occurred across the U.S. central plains on 4–5 June 2005. The experiment employs the Weather Research and Forecasting Model at 4-km horizontal grid spacing and the ensemble adjustment Kalman filter algorithm in the Data Assimilation Research Testbed system. The ability of GOES-R ABI brightness temperatures to improve the analysis and forecast accuracy when assimilated separately or simultaneously with Doppler radar reflectivity and radial velocity observations was assessed, along with the use of bias correction and different covariance localization radii for the brightness temperatures. Results show that the radar observations accurately capture the structure of a portion of the storm complex by the end of the assimilation period, but that more of the storms and atmospheric features are reproduced and the accuracy of the ensuing forecast improved when the brightness temperatures are also assimilated.

Radiometric Inter-Calibration between Himawari-8 AHI and S-NPP VIIRS for the Solar Reflective Bands

The Advanced Himawari Imager (AHI) on-board Himawari-8, which was launched on 7 October 2014, is the first geostationary instrument housed with a solar diffuser to provide accurate onboard calibrated data for the visible and near-infrared (VNIR) bands. In this study, the Ray-matching and collocated Deep Convective Cloud (DCC) methods, both of which are based on incidently collocated homogeneous pairs between AHI and Suomi NPP (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), are used to evaluate the calibration difference between these two instruments. While the Ray-matching method is used to examine the reflectance difference over the all-sky collocations with similar viewing and illumination geometries, the near lambertian collocated DCC pxiels are used to examine the difference for the median or high reflectance scenes. Strong linear relationships between AHI and VIIRS can be found at all the paired AHI and VIIRS bands. Results of both methods indicate that AHI radiometric calibration accuracy agrees well with VIIRS data within 5% for B1-4 and B6 at mid and high reflectance scenes, while AHI B5 is generally brighter than VIIRS by ~6%–8%. No apparent East-West viewing angle dependent calibration difference can be found at all the VNIR bands. Compared to the Ray-matching method, the collocated DCC method provides less uncertainty of inter-calibration results at near-infrared (NIR) bands. As AHI has similar optics and calibration designs to the GOES-R Advanced Baseline Imager (ABI), which is currently scheduled to launch in fall 2016, the on-orbit AHI data provides a unique opportunity to develop, test and examine the cal/val tools developed for ABI.

Development and validation of satellite-based estimates of surface visibility

Abstract. A satellite-based surface visibility retrieval has been developed using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements as a proxy for Advanced Baseline Imager (ABI) data from the next generation of Geostationary Operational Environmental Satellites (GOES-R). The retrieval uses a multiple linear regression approach to relate satellite aerosol optical depth, fog/low cloud probability and thickness retrievals, and meteorological variables from numerical weather prediction forecasts to National Weather Service Automated Surface Observing System (ASOS) surface visibility measurements. Validation using independent ASOS measurements shows that the GOES-R ABI surface visibility retrieval (V) has an overall success rate of 64.5 % for classifying clear (V ≥ 30 km), moderate (10 km ≤ V < 30 km), low (2 km ≤ V < 10 km), and poor (V < 2 km) visibilities and shows the most skill during June through September, when Heidke skill scores are between 0.2 and 0.4. We demonstrate that the aerosol (clear-sky) component of the GOES-R ABI visibility retrieval can be used to augment measurements from the United States Environmental Protection Agency (EPA) and National Park Service (NPS) Interagency Monitoring of Protected Visual Environments (IMPROVE) network and provide useful information to the regional planning offices responsible for developing mitigation strategies required under the EPA's Regional Haze Rule, particularly during regional haze events associated with smoke from wildfires.

Comparison of the Calibration Algorithms and SI Traceability of MODIS, VIIRS, GOES, and GOES-R ABI Sensors

The radiometric calibration equations for the thermal emissive bands (TEB) and the reflective solar bands (RSB) measurements of the earth scenes by the polar satellite sensors, (Terra and Aqua) MODIS and Suomi NPP (VIIRS), and geostationary sensors, GOES Imager and the GOES-R Advanced Baseline Imager (ABI) are analyzed towards calibration algorithm harmonization on the basis of SI traceability which is one of the goals of the NOAA National Calibration Center (NCC). One of the overarching goals of NCC is to provide knowledge base on the NOAA operational satellite sensors and recommend best practices for achieving SI traceability for the radiance measurements on-orbit. As such, the calibration methodologies of these satellite optical sensors are reviewed in light of the recommended practice for radiometric calibration at the National Institute of Standards and Technology (NIST). The equivalence of some of the spectral bands in these sensors for their end products is presented. The operational and calibration features of the sensors for on-orbit observation of radiance are also compared in tabular form. This review is also to serve as a quick cross reference to researchers and analysts on how the observed signals from these sensors in space are converted to radiances.

Examining Deep Convective Cloud Evolution Using Total Lightning, WSR-88D, and GOES-14 Super Rapid Scan Datasets*

Abstract The Geostationary Operational Environmental Satellite-14 (GOES-14) Imager operated in 1-min Super Rapid Scan Operations for GOES-R (SRSOR) mode during summer and fall of 2012 to emulate the high temporal resolution sampling of the GOES-R Advanced Baseline Imager (ABI). The current GOES operational scan interval is 15–30 min, which is too coarse to capture details important for severe convective storm forecasting including 1) when indicators of a severe storm such as rapid cloud-top cooling, overshooting tops, and above-anvil cirrus plumes first appear; 2) how satellite-observed cloud tops truly evolve over time; and 3) how satellite cloud-top observations compare with radar and lightning observations at high temporal resolution. In this paper, SRSOR data, radar, and lightning observations are used to analyze five convective storms, four of which were severe, to address these uncertainties. GOES cloud-top cooling, increased lightning flash rates, and peak precipitation echo tops often preceded severe weather, signaling rapid intensification of the storm updraft. Near the time of several severe hail or damaging wind events, GOES cloud-top temperatures and radar echo tops were warming rapidly, which indicated variability in the storm updraft that could have allowed the hail and wind gusts to reach the surface. Above-anvil cirrus plumes were another prominent indicator of impending severe weather. Detailed analysis of storms throughout the 2012 SRSOR period indicates that 57% of the plume-producing storms were severe and 85% of plumes from severe storms appeared before a severe weather report with an average lead time of 18 min, 9 min earlier than what would be observed by GOES operational scanning.

Evaluation of the GOES-R ABI LAP Retrieval Algorithm Using the GOES-13 Sounder

Abstract A physical retrieval algorithm has been developed for deriving the legacy atmospheric profile (LAP) product from infrared radiances of the Advanced Baseline Imager (ABI) on board the next-generation Geostationary Operational Environmental Satellite (GOES-R) series. In this study, the GOES-R ABI LAP retrieval algorithm is applied to the GOES-13 sounder radiance measurements (termed the GOES-13 LAP retrieval algorithm in this study) for its validation as well as for potential transition of the GOES-13 LAP retrieval algorithm for the operational processing of GOES sounder data. The GOES-13 LAP retrievals are compared with five different truth measurements: radiosonde observation (raob) and microwave radiometer–measured total precipitable water (TPW) at the Atmospheric Radiation Measurement Cloud and Radiation Testbed site, conventional raob, TPW measurements from the global positioning system–integrated precipitable water NOAA network, and TPW measurements from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The results show that with the GOES-R ABI LAP retrieval algorithm, the GOES-13 sounder provides better water vapor profiles than the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecast fields at the levels between 300 and 700 hPa. The root-mean-square error (RMSE) and standard deviation (STD) of the GOES-13 sounder TPW are consistently reduced from those of the GFS forecast no matter which measurements are used as the truth. These substantial improvements indicate that the GOES-R ABI LAP retrieval algorithm is well prepared to provide continuity of quality to some of the current GOES sounder products, and the algorithm can be transferred to process the current GOES sounder measurements for operational product generation.

Use of MODIS 16-day albedos in generating GOES-R advanced baseline imager imagery

Advanced preparation for satellite data from the next-generation GOES-R advanced baseline imager (ABI) is supported by coupling high resolution mesoscale and radiative transfer numerical models. Calculated GOES-R ABI imagery is produced in a two-step process. First, a mesoscale model is used to simulate an event over a region with 400 m horizontal grid spacings; secondly, output from the mesoscale model is used as input to a second model that calculates top of the atmosphere radiances at selected GOES-R ABI wavelengths. Such radiances or brightness temperatures are referred to as synthetic imagery. In order for the synthetic imagery to contain realistic horizontal variability of values of surface reflectance at wavelengths from 0.44 to 2.25 μm, MODIS 16-day albedos are incorporated in the radiative transfer calculations. One application of synthetic GOES-R imagery is that of algorithm development and testing. Algorithms may focus on, but are not limited to, the detection and retrieval of smoke, volcanic ash, fires, blowing dust, and the state of surface physiography. Proper identification of such features is, at times, dependent on the horizontal variability of surface reflectance values. MODIS 16-day spectrally dependent albedos are a valuable dataset in aiding the generation of synthetic GOES-R imagery.

A case for natural colour imagery from geostationary satellites, and an approximation for the GOES-R ABI

‘Natural’ (or ‘true’) colour imagery, so-called for its qualitative likeness to colour photography, is one of the most visually intuitive and readily communicable forms of satellite information. It is constructed by combining solar reflectance measurements from three narrow spectral bands defining the red, green and blue wavelengths of visible light. Natural colour facilitates the interpretation of multiple components in the complex earth/atmosphere scene and, therefore, it is widely used by experts and non-experts alike to visualize many forms of geophysical phenomena. Although sensors on board low-Earth-orbiting (LEO) satellites have long-demonstrated the superior quality of natural colour imagery over various other ‘false colour’ renditions, similar capabilities currently do not exist on sensors operating in geostationary orbits that offer distinct advantages over LEO in terms of high temporal refresh. The Advanced Baseline Imager (ABI) of the next-generation Geostationary Operational Environmental Satellite (GOES)-R series will include the blue and red bands, but is missing the 0.55 μm green band necessary for producing natural colour. The emphases of this article are twofold. First, we consider the merits of natural colour imagery from the standpoints of both science and operational users, and the philosophical roadblocks of a system definition process that seems inherently ill-equipped to consider qualitative user requirements. Second, we present a mitigation strategy for GOES-R ABI that entails synthesizing the missing ABI green band information via its correlation with spectrally adjacent available bands, with a first-order account for surface type dependencies. The technique is developed, demonstrated and evaluated here using Moderate-resolution Imaging Spectroradiometer (MODIS) data.

Synthetic advanced baseline imager true-color imagery

True-color imagery, which is formed via a weighted combination of red, green, and blue (RGB) spectral information, has important operational applications for qualitative environmental characterization, including the detection of smoke plumes, volcanic ash, and other aerosols that are not as easily discerned in conventional visible or infrared imagery, but may be more readily characterized via color properties. Despite its universal popularity, true-color is currently unavailable from geostationary satellites, and the next-generation GOES-R advanced baseline imager (ABI) will fall one band (green; 0.55 μm) short of doing so. However, approximations exist, and a process for simulating true-color imagery representative of capabilities anticipated from the ABI is presented and assessed here. High-resolution atmospheric model simulations are used to produce the ABI reflective band imagery required for true-color imagery. Those simulations are then rendered at ABI spatial (0.5-km visible) and temporal (5 min) resolution, to provide realistic data, long before the anticipated 2015 launch of GOES-R. An additional analysis, a color-space transformation, is used to assess the true-color (RGB) ABI images. The resulting hue images verify the less-green bias in the synthetic-green band and synthetic-RGB images created on ABI simulated data. Assessing the deficiencies in the RGB process will hopefully lead to an improved and standard means for generating an RGB product from the ABI data stream. Finally, as one of the many product applications of true-color imagery, an example of synthetic true-color imagery with added smoke is presented. The incorporation of aerosol properties into simulated imagery may help reveal the limits of detectability for atmospheric aerosols with future ABI.

A Method for Estimating Clear-Sky Instantaneous Land-Surface Longwave Radiation With GOES Sounder and GOES-R ABI Data

This letter presents new models for estimating clear-sky instantaneous longwave radiation over land surfaces using the Geostationary Operational Environmental Satellites (GOES) Sounders and GOES-R Advanced Baseline Imager (ABI) thermal infrared top-of-atmosphere (TOA) radiances. The method used in this study shares the same hybrid method framework designed for Moderate Resolution Imaging Spectroradiometer. We propose separate surface downward longwave radiation (LWDN) and upwelling longwave radiation (LWUP) models because the two components are dominated by different surface/atmospheric properties. A nonlinear model was developed to estimate LWDN, and a linear model was developed to estimate LWUP. The GOES-12 Sounder-derived LWDN, LWUP, and surface net longwave radiation (LWNT = LWUP-LWDN) were evaluated using one full-year of ground data from the Surface Radiation Budget Network. The root-mean-squared errors (rmses) are less than 22.03 W/m2 at all four sites. Our study indicates that the hybrid method can also be applied to estimate LWUP using the future GOES-R ABI TOA radiances. The lack of a channel beyond 13.3 m in the proposed ABI design may cause larger rmses when estimating LWDN.

The GOES-R Advanced Baseline Imager and the Continuation of Current Sounder Products

Abstract The first of the next-generation series of Geostationary Operational Environmental Satellites (GOES-R) is scheduled for launch in the 2015 time frame. One of the primary instruments on GOES-R, the Advanced Baseline Imager (ABI), will offer more spectral bands, higher spatial resolution, and faster imaging than does the current GOES Imager. Measurements from the ABI will be used for a wide range of qualitative and quantitative weather, land, ocean, cryosphere, environmental, and climate applications. However, the first and, likely, the second of the new series of GOES will not carry an infrared sounder dedicated to acquiring high-vertical-resolution atmospheric temperature and humidity profiles that are key to mesoscale and regional severe-weather forecasting. The ABI will provide some continuity of the current sounder products to bridge the gap until the advent of the GOES advanced infrared sounder. Both theoretical analysis and retrieval simulations show that data from the ABI can be combined with temperature and moisture information from forecast models to produce derived products that will be adequate substitutes for the legacy products from the current GOES sounders. Products generated from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements also demonstrate the utility of those legacy products for nowcasting applications. However, because of very coarse vertical resolution and limited accuracy in the legacy sounding products, placing a hyperspectral-resolution infrared sounder with high temporal resolution on future GOES is an essential step toward realizing substantial improvements in mesoscale and severe-weather forecasting required by the user communities.

GOES-R Advanced Baseline Imager Color Product Development

Abstract The current Geostationary Operational Environmental Satellite (GOES) series was inaugurated in 1994 with the launch of GOES-8 and will continue with two more satellites (GOES-O and -P) after the most recent GOES-13 launched in 2006. The next-generation GOES (beginning with GOES-R) will be launched in the 2015 time frame. This new series of satellites will include improved spatial, temporal, spectral, and radiometric resolution. The last two characteristics are manifest by an increased number of spectral bands and increased precision for measurements from those bands. To take advantage of the lead time needed to design, build, and test this new and complex satellite system, work is going into developing image products to be implemented as soon as GOES-R becomes operational. Preparations for GOES-R image products for applications to various weather events, especially mesoscale events, are well underway. The approach used for these “risk reduction” activities is to apply data from existing operational and experimental satellites (both polar orbiting and geostationary) to create image products that will emulate those to be available from GOES-R as closely as possible. Those image products can either be new products or improvements leveraged on existing operational products. In this article, the new GOES-R Advanced Baseline Imager is briefly reviewed, and the evolutionary development of two qualitative products—one for the detection of fog and stratus, and the other for blowing dust—is presented. Emphasis is on the evolutionary development of these mesoscale products and possible quantitative discrimination among the various image features that are seen.