Geostationary Operational Environmental Satellite Research Articles

Method for assessment of the apparent detector stack position in a whisk-broom sensor

In a whisk-broom sensor, such as the Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite (GOES)-R series of geostationary satellites, the sensor optics projects the observed scene onto the sensor focal plane arrays (FPA), with the detectors arranged in a column perpendicular to the scan direction. An assessment system compares the images made by the sensor with known landmarks and calculates the image deviations from the landmark positions. These deviations are called navigation residuals, and in this work they are interpreted in a broad sense as deviations of the actual from the ideal optical paths. The deviations can be caused by several issues, such as a sensor pointing error, deficiency in sensor optics, and inaccuracy of detector locations on FPA. As the ABI sensor scans the Earth to make a full-disk image in 22 parallel swaths, we refer the landmark residuals from all swaths to the observing detector positions in the ABI detector column. This interpretation inverts the retrieval of navigation residuals and gives deviations of the image from the expected ideal position on an ideal FPA. As the errors of pointing and sensor optics can be assessed independently, the remaining deviations can be interpreted as these with respect to the ideal detector position map (in other words, to the pre-launch assessment). We call these deviations the apparent detector positions. Knowledge of these deviations will greatly simplify the navigation commissioning for future ABI sensors and similar devices. Using our archive of ABI image navigation data for ABI sensor on the GOES-16 satellite, we show the wealth of additional information about the sensor on-orbit condition that may be extracted with the proposed method. This algorithm can use any image navigation system that is sufficiently precise.

The Response of the Earth’s Lower Ionosphere to Gamma-Ray Solar Flares and their Associated X-ray

The present work aims to explore the impact of solar gamma-ray flares (GRFs), detected by the Fermi-Large Area Telescope (LAT), and their associated X-ray flares (XRFs) on the Earth’s lower ionosphere. The data of ionospheric parameter fmin were collected from the Yamagawa mid-latitude ionosonde station in Japan. Because most of the GRFs used in this study are associated with soft X-ray (SXR) emissions, detected by the Geostationary Operational Environmental Satellite (GOES), we classified them into three types: simultaneous (S), delayed (D) and not associated (N), reflecting the phase occurrence of the solar flares. Generally, the ionospheric D layer responds strongly to XRFs more than GRFs. The temporal profiles of the ionospheric parameter fmin show distinct behaviors as a response to the various GRF types. In the case of the S-type, a single strong peak, with an average value of 5.2 MHz, emanates in the fmin profile within an average interval of time of less than half an hour. In the case of the D-type, two successive peaks appear in the fmin profile, with an average value of 6.0 MHz for the second peak through a time interval of one hour after the onset of gamma-ray (GR) emissions. The response of the fmin parameter to the N-type flares appears as a single peak with an average value of 4.8 MHz within about an hour. We notice that the difference between the fmin peak values and the corresponding reference ones (dfmin) is more convenient and representative than the fmin values, where the median GR flux of the S-, D- and N-types tends to increase as the average dfmin increases.

Linking Synoptic Patterns to Cloud Properties and Local Circulations Over Southeastern Texas

AbstractThis study classifies meteorological regimes in the southeastern Texas region to identify environmental conditions that favor sea‐breeze induced convection. The classification is accomplished using a Self‐Organizing Map (SOM) approach. We applied SOM to 10 years of 700‐hPa geopotential height anomalies during the summer months from reanalysis data to distinguish three dominant synoptic regimes, with a continuum of transitional states between those. The primary regimes include: (a) a pre‐trough regime associated with a synoptic trough, (b) a post‐trough regime with upper‐level northerly flow, and (c) an anticyclonic regime within the westward extent of the Bermuda High. We project the data from the Geostationary Operational Environmental Satellite and the Next Generation Weather Radar system onto each SOM node to investigate the characteristics of cloud and precipitation properties in different regimes. When southeastern Texas is positioned to the southwest quadrant of a maritime high pressure system, an increased cloud frequency is observed over the region during the afternoon hours due to significant moisture advection. A confluence of synoptic southerly flow and sea‐breeze circulation commonly occurs in this regime. When a high pressure system is over southeastern Texas, the area is dominated by large‐scale subsidence with weak pressure gradients and moderate precipitable water vapor. This weak synoptic forcing is favorable for the formation of a sea‐breeze circulation. This is confirmed by an enhanced onshore flow and a decreased temperature at the surface in the early afternoon, as well as a sharp increase in radar echo top height.

Assessing the Forecast Impact of a Geostationary Microwave Sounder Using Regional and Global OSSEs

Abstract Forecast observing system simulation experiments (OSSEs) are conducted to assess the potential impact of geostationary microwave (GeoMW) sounder observations on numerical weather prediction forecasts. A regional OSSE is conducted using a tropical cyclone (TC) case that is very similar to Hurricane Harvey (2017), as hurricanes are among the most devastating of weather-related natural disasters, and hurricane intensity continues to pose a significant challenge for numerical weather prediction. A global OSSE is conducted to assess the potential impact of a single GeoMW sounder centered over the continental United States versus two sounders positioned at the current locations of the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES) East and West. It is found that assimilation of GeoMW soundings result in better characterization of the TC environment, especially before and during intensification, which leads to significant improvements in forecasts of TC track and intensity. TC vertical structure (warm core thermal perturbation and horizontal wind distribution) is also substantially improved, as are the surface wind and precipitation extremes. In the global OSSE, assimilation of GeoMW soundings leads to slight improvement globally and significant improvement regionally, with regional impact equal to or greater than nearly all other observation types. Significance Statement This work seeks to determine the impact of a new geostationary microwave (GeoMW) sounder on tropical cyclone forecasts in particular, and on weather forecasts in general. It does so by assimilating simulated GeoMW sounder data into two different forecast models: one global and one regional. The data have a small positive impact globally, and a significant positive impact over the region viewed by the GeoMW instrument. In particular, assimilation of GeoMW data has a significant and positive impact on forecasts of tropical cyclone track, strength, and structure.

Cutoff Latitudes of Solar Proton Events Measured by GPS Satellites

AbstractSolar energetic particles (SEPs), one of the main causes of particle radiation in interplanetary space, can disrupt radio communication, induce spacecraft failures and change the heating and cooling rates in the atmosphere among others. To investigate the impact of SEPs and more specifically solar proton events (SPEs), we established a cutoff latitude database based on energetic particle data from Combined X‐ray Dosimeters (CXDs) on board the Global Positioning System (GPS) spacecraft. Introducing a novel normalization method involving proton fluxes from the Geostationary Operational Environmental Satellites enabled us to include the CXD data from its introduction (2001) onwards. The database contains 5714 cutoff latitudes divided over six energies between 18 and 115 MeV which occur during 58 SPEs from 2001 to 2015. Based on the database, a cutoff latitude parameterization as a function of solar wind dynamic pressure and geomagnetic indices Kp and Dst is created for each energy. Moreover, comparisons to previous studies on energetic particle data from the Polar Orbiting Environmental Satellites have been performed to put the GPS data into perspective. A 1–2° poleward offset is found for the GPS based cutoff latitude models, for which several causes are discussed. Furthermore, the limitation of GPS data to geomagnetic latitudes above 60° should be considered. All in all, the usage of the long time span of GPS data in this study combined with its recent release (2016) opens up a new range of studies involving GPS energetic particle data such as investigating long‐term trends with respect to our solar cycle or magnetospheric trends.

Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance

Abstract. In order to aid feature selection in thunderstorm nowcasting, we present an analysis of the utility of various sources of data for machine-learning-based nowcasting of hazards related to thunderstorms. We considered ground-based radar data, satellite-based imagery and lightning observations, forecast data from numerical weather prediction (NWP) and the topography from a digital elevation model (DEM), ending up with 106 different predictive variables. We evaluated machine-learning models to nowcast storm track radar reflectivity (representing precipitation), lightning occurrence, and the 45 dBZ radar echo top height that can be used as an indicator of hail, producing predictions for lead times of up to 60 min. The study was carried out in an area in the Northeastern United States for which observations from the Geostationary Operational Environmental Satellite-16 are available and can be used as a proxy for the upcoming Meteosat Third Generation capabilities in Europe. The benefits of the data sources were evaluated using two complementary approaches: using feature importance reported by the machine learning model based on gradient-boosted trees, and by repeating the analysis using all possible combinations of the data sources. The two approaches sometimes yielded seemingly contradictory results, as the feature importance reported by the gradient-boosting algorithm sometimes disregards certain features that are still useful in the absence of more powerful predictors, while, at times, it overstates the importance of other features. We found that the radar data is the most important predictor overall. The satellite imagery is beneficial for all of the studied predictands, and therefore offers a viable alternative in regions where radar data are unavailable, such as over the oceans and in less-developed ares. The lightning data are very useful for nowcasting lightning but are of limited use for the other hazards. While the feature importance ranks NWP data as an important input, the omission of NWP data can be well compensated for by using information in the observational data over the nowcast period. Finally, we did not find evidence that the nowcast benefits from the DEM data.

The Dependence of Solar Flare Magnitude on sunspot Area During Activity Cycle 24

Abstract We measure the sunspot areas of activity cycle 24 using ten years of continuum images from the Helioseismic and Magnetic Imager, and compare them with the peak flare soft X-ray flux from the Geostationary Operational Environmental Satellite. We find that the sunspot area in our sample is positively correlated with the magnitude of the largest flare they produce. Complex spot groups with βγδ magnetic classification tend to be larger and more likely to produce intense flares. Our findings are qualitatively consistent with previous studies.

Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City

The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the ability to improve understanding of the surface energy balance is limited by the heterogeneity of surfaces in urban areas. This study aims to enhance the understanding of the urban surface energy budget through an innovation in the use of land surface temperature (LST) observations from remote sensing satellites. A LST database with 5–min temporal and 30–m spatial resolution is developed by spatial downscaling of the Geostationary Operational Environmental Satellites—R (GOES–R) series LST product over New York City (NYC). The new downscaling method, known as the Spatial Downscaling Method (SDM), benefits from the fine spatial resolution of Landsat–8 and high temporal resolution of GOES–R, and considers the temporal variation in LST for each land cover type separately. Preliminary results show that the SDM can reproduce the temporal and spatial variability of LST over NYC reasonably well and the downscaled LST has a spatial root mean square error (RMSE) of the order of 2 K as compared to the independent Landsat–8 observations. The SDM shows smaller RMSE of 1.93 K over the tree canopy land cover, whereas RMSE is 2.19 K for built–up areas. The overall results indicate that the SDM has potential to estimate LST at finer spatial and temporal scales over urban regions.

An initial assessment of the distribution of total Flash Rate Density (FRD) in Brazil from GOES-16 Geostationary Lightning Mapper (GLM) observations

Brazil is one of the regions in the world where lightning most occurs. With the launch of the Geostationary Environmental Operational Satellite – 16 (GOES-16), it was possible, for the first time, to carry out an almost uniform resolution mapping, in near real time, of total lightning with a field of view that covers most of the western hemisphere. The objective of this study was to evaluate the initial spatio-temporal distribution of the Total Lightning Flash Rate Density (FRD) for all of Brazil, and to determine lightning hotspots in cities in five regions, the South, the Southeast, the Midwest, the Northeast, and the North. Total lightning flash data from the Geostationary Lightning Mapper (GLM) sensor on board the GOES-16 were evaluated for a three-years period (from 2018 to 2020). The greatest FRD (about 30 fl km−2 season−1) occurred during the hot season (between spring and summer), mostly in the late afternoon (12–18 Local Hour, LH), in the northwest-southeastern portion of the country (AM, PA, MT, GO and TO), in the southeastern of RJ and closer to the Brazil-Argentina border (MS, PR, SC and RS). The lowest FRD values were observed along coastal regions, extending from the north of RJ to AP, and for the north of RO and PA. São Félix do Xingu city in Pará came in first (131 fl km−2 year−1) in the national ranking for lightning hotspots, showing well-defined monthly and daytime cycles with maximums at 14 LH.

Combining GOES-R and ECOSTRESS land surface temperature data to investigate diurnal variations of surface urban heat island

The surface urban heat island (SUHI) phenomenon is characterized by both high spatial and temporal variability, while its diurnal (i.e., diel) variations have rarely been investigated because traditional satellites and sensors flying on polar orbits (e.g., Landsat, MODIS) have no diurnal sampling capability. Here we combined land surface temperature (LST) data from the Geostationary Operational Environmental Satellites (GOES-R) and the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to explore the diurnal variations of SUHI and thermal differentiation among various land covers over the Boston Metropolitan Area. With the combined use of the LST data from GOES-R and ECOSTRESS, we took advantage of the strengths of both GOES-R (i.e., high frequency in each day and night) and ECOSTRESS (i.e., much finer spatial resolution). The SUHI intensity of the urban-core and suburban areas both exhibited clear diurnal patterns for different seasons: a continuous increase in the SUHI intensity from sunrise to noon and a decrease thereafter to sunset, followed by a relatively low and constant intensity during nighttime. The LST contrasts among different land cover types were clearly larger in the daytime than at nighttime and peaked around midday. At noon in summer, the LST of ‘Developed, High Intensity’ was 2.6 °C higher than that of ‘Developed, Medium Intensity’, and about 4.6 °C higher than that of “Developed, Open Space” and “Developed, Low Intensity”. Controlling the percent impervious surface in construction land at a relatively low level (e.g., below ~49%) could effectively alleviate the impacts of SUHI. Compared with GOES-R data, ECOSTRESS LST is suitable for monitoring the diurnal variations of intracity thermal environment at the subdistrict (or neighborhood) scale. Our study highlights the value of the combined use of geostationary satellite and ECOSTRESS LST in exploring the diurnal cycling of the SUHI, and can help inform urban planning and land-based climate mitigation policies in the context of climate change.

Acceleration of Solar Energetic Particles through CME-driven Shock and Streamer Interaction

Abstract On 2013 June 21, a solar prominence eruption was observed, accompanied by an M2.9 class flare, a fast coronal mass ejection, and a type II radio burst. The concomitant emission of solar energetic particles (SEPs) produced a significant proton flux increase, in the energy range 4–100 MeV, measured by the Low and High Energy Telescopes on board the Solar TErrestrial RElations Observatory (STEREO)-B spacecraft. Only small enhancements, at lower energies, were observed at the STEREO-A and Geostationary Operational Environmental Satellite (GOES) spacecraft. This work investigates the relationship between the expanding front, coronal streamers, and the SEP fluxes observed at different locations. Extreme-ultraviolet data, acquired by the Atmospheric Imaging Assembly (AIA) instrument on board the Solar Dynamics Observatory (SDO), were used to study the expanding front and its interaction with streamer structures in the low corona. The 3D shape of the expanding front was reconstructed and extrapolated at different times by using SDO/AIA, STEREO/Sun Earth Connection Coronal and Heliospheric Investigation, and Solar and Heliospheric Observatory/Large Angle and Spectrometric Coronagraph observations with a spheroidal model. By adopting a potential field source surface approximation and estimating the magnetic connection of the Parker spiral, below and above 2.5 R ⊙, we found that during the early expansion of the eruption, the front had a strong magnetic connection with STEREO-B (between the nose and flank of the eruption front) while having a weak connection with STEREO-A and GOES. The obtained results provide evidence, for the first time, that the interaction between an expanding front and streamer structures can be responsible for the acceleration of high-energy SEPs up to at least 100 MeV, as it favors particle trapping and hence increases the shock acceleration efficiency.

Ensemble Numerical Simulations of Realistic SEP Events and the Inspiration for Space Weather Awareness

The solar energetic particle (SEP) event is a kind of hazardous space weather phenomena, so its quantitative forecast is of great importance from the aspect of space environmental situation awareness. We present here a set of SEP forecast tools, which consists of three components : (1) a simple polytropic solar wind model to estimate the background solar wind conditions at the inner boundary of 0.1 AU (about 20 R ⊙); (2) an ice-cream-cone model to estimate the erupted coronal mass ejection (CME) parameters; and (3) the improved Particle Acceleration and Transport in the Heliosphere (iPATH) model to calculate particle fluxes and energy spectra. By utilizing the above models, we have simulated six realistic SEP events from 2010 August 14 to 2014 September 10, and compared the simulated results to the Geostationary Operational Environmental Satellites (GOES) spacecraft observations. The results show that the simulated fluxes of >10 MeV particles agree with the observations while the simulated fluxes of >100 MeV particles are higher than the observed data. One of the possible reasons is that we have adopted a simple method in the model to calculate the injection rate of energetic particles. Furthermore, we have conducted the ensemble numerical simulations over these events and investigated the effects of different background solar wind conditions at the inner boundary on SEP events. The results imply that the initial CME density plays an important role in determining the power spectrum, while the effect of varying background solar wind temperature is not significant. Naturally, we have examined the influence of CME initial density on the numerical prediction results for virtual SEP cases with different CME ejection speeds. The result shows that the effect of initial CME density variation is inversely associated with CME speed.

A New Magnetic Parameter of Active Regions Distinguishing Large Eruptive and Confined Solar Flares

Abstract With the aim of investigating how the magnetic field in solar active regions (ARs) controls flare activity, i.e., whether a confined or eruptive flare occurs, we analyze 106 flares of Geostationary Operational Environmental Satellite class ≥M1.0 during 2010–2019. We calculate mean characteristic twist parameters α FPIL within the “flaring polarity inversion line” region and α HFED within the area of high photospheric magnetic free energy density, which both provide measures of the nonpotentiality of the AR core region. Magnetic twist is thought to be related to the driving force of electric current-driven instabilities, such as the helical kink instability. We also calculate total unsigned magnetic flux (ΦAR) of ARs producing the flare, which describes the strength of the background field confinement. By considering both the constraining effect of background magnetic fields and the magnetic nonpotentiality of ARs, we propose a new parameter α/ΦAR to measure the probability for a large flare to be associated with a coronal mass ejection (CME). We find that in about 90% of eruptive flares, α FPIL/ΦAR and α HFED/ΦAR are beyond critical values (2.2 × 10−24 and 3.2 × 10−24 Mm−1 Mx−1), whereas they are less than critical values in ∼80% of confined flares. This indicates that the new parameter α/ΦAR is well able to distinguish eruptive flares from confined flares. Our investigation suggests that the relative measure of magnetic nonpotentiality within the AR core over the restriction of the background field largely controls the capability of ARs to produce eruptive flares.

Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM2.5 levels during the Camp Fire episode in California

Wildland fire smoke contains large amounts of PM2.5 that can traverse tens to hundreds of kilometers, resulting in significant deterioration of air quality and excess mortality and morbidity in downwind regions. Estimating PM2.5 levels while considering the impact of wildfire smoke has been challenging due to the lack of ground monitoring coverage near the smoke plumes. We aim to estimate total PM2.5 concentration during the Camp Fire episode, the deadliest wildland fire in California history. Our random forest (RF) model combines calibrated low-cost sensor data (PurpleAir) with regulatory monitor measurements (Air Quality System, AQS) to bolster ground observations, Geostationary Operational Environmental Satellite-16 (GOES-16)’s high temporal resolution to achieve hourly predictions, and oversampling techniques (Synthetic Minority Oversampling Technique, SMOTE) to reduce model underestimation at high PM2.5 levels. In addition, meteorological fields at 3 km resolution from the High-Resolution Rapid Refresh model and land use variables were also included in the model. Our AQS-only model achieved an out of bag (OOB) R2 (RMSE) of 0.84 (12.00 μg/m3) and spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 μg/m3) and 0.73 (16.58 μg/m3), respectively. Our AQS + Weighted PurpleAir Model achieved OOB R2 (RMSE) of 0.86 (9.52 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.75 (14.93 μg/m3) and 0.79 (11.89 μg/m3), respectively. Our AQS + Weighted PurpleAir + SMOTE Model achieved OOB R2 (RMSE) of 0.92 (10.44 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.84 (12.36 μg/m3) and 0.85 (14.88 μg/m3), respectively. Hourly predictions from our model may aid in epidemiological investigations of intense and acute exposure to PM2.5 during the Camp Fire episode.

High Spatial-Temporal PM2.5 Modeling Utilizing Next Generation Weather Radar (NEXRAD) as a Supplementary Weather Source

PM2.5, a type of fine particulate with a diameter equal to or less than 2.5 micrometers, has been identified as a major source of air pollution, and is associated with many health issues. Research on utilizing various data sources, such as remote sensing and in situ sensors, for PM2.5 concentrations modeling remains a hot topic. In this study, the Next Generation Weather Radar (NEXRAD) is used as a supplementary weather data source, along with European Centre for Medium-Range Weather Forecasts (ECMWF), solar angles, and Geostationary Operational Environmental Satellite (GOES16) Aerosol Optical Depth (AOD) to model high spatial-temporal PM2.5 concentrations. PM2.5 concentrations as well as in situ weather condition variables are collected from the 31 sensors that are deployed in the Dallas Metropolitan area. Four machine learning models with different predictor variables are developed based on an ensemble approach. Since in situ weather observations are not widely available, ECMWF is used as an alternative data source for weather conditions in studies. Hence, the four established models are compared in three groups. Both models in this first group use weather variables collected from deployed sensors, but one uses NEXRAD and the other does not. In the second group, the two models use weather variables retrieved from ECMWF, one using NEXRAD and one without. In the third group, one model uses weather variables from ECMWF, and the other uses in situ weather variables, both without NEXRAD. The first two environmental groups investigate how NEXRAD can enhance model performances with weather variables collected from in situ observations and ECMWF, respectively. The third group explores how effective using ECMWF as an alternative source of weather conditions. Based on the results, the incorporation of NEXRAD achieves an R2 score of 0.86 and 0.83 for groups 1 and 2, respectively, for an improvement of 2.8% and 9.6% over those models without NEXRAD. For group three, the use of ECMWF as an alternative source of in situ weather observations results in a 0.13 R2 drop. For PM2.5 estimation, weather variables including precipitation, temperature, pressure, and surface pressure from ECMWF and deployed sensors, as well as NEXRAD velocity, are shown to be significant factors.

Iterative assimilation of geostationary satellite observations in retrospective meteorological modeling for air quality studies

Clouds impact many aspects of both the physical and chemical atmosphere at many different spatial and temporal scales. Because of this, their accurate representation within numerical weather prediction (NWP) models is vital. In this study, a previously developed assimilation technique for assimilating Geostationary Operational Environmental Satellite (GOES) derived cloud fields into the Weather Research and Forecasting (WRF) meteorological model was tested over the August–September 2013 time period, on a 12-km domain covering the contiguous United States (CONUS). At the same time, additional improvements to the assimilation technique were introduced to account for the model cloud time tendency. The improvements resulted in more consistent cloud fields, while also improving the model surface statistics when compared to the original technique. The results indicate that both implementations of the assimilation technique improve the agreement between the model-predicted and GOES-derived cloud fields, but the additional refinements significantly improve the overall model performance. The daily average percentage increase in the cloud agreement was 6.53% for the original technique, compared to 11.27% for the refined technique. With the improvement in the model cloud fields, the average error in the predicted solar irradiance across the CONUS domain was reduced by 4.4 W m−2 and 24.6 W m−2 for the original and revised assimilation techniques, respectively. The revised assimilation technique also reduced the slight degradation in the surface statistics of wind speed, temperature, and mixing ratio that was created with the original technique. This resulted in surface error statistics that were nearly the same as a control simulation, but with improved model cloud performance.

A Multi-Year Study of GOES-13 Droplet Effective Radius Retrievals for Warm Clouds over South America and Southeast Pacific

Geostationary satellites can retrieve the cloud droplet effective radius (re) but suffer biases from cloud inhomogeneities, internal retrieval nonlinearities, and 3-D scattering/shadowing from neighboring clouds, among others. A 1-D retrieval method was applied to Geostationary Operational Environmental Satellite 13 (GOES-13) imagery, over large areas in South America (5∘ N–30∘ S; 20∘–70∘ W), the Southeast Pacific (5∘ N–30∘ S; 70∘–120∘ W), and the Amazon (2∘ N–7∘ S; 54∘–73∘ W), for four months in each year from 2014–2017. Results were compared against in situ aircraft measurements and the Moderate Resolution Imaging Spectroradiometer cloud product for Terra and Aqua satellites. Monthly regression parameters approximately followed a seasonal pattern. With up to 108,009 of matchups, slope, intercept, and correlation for Terra (Aqua) ranged from about 0.71 to 1.17, −2.8 to 2.5 μm, and 0.61 to 0.91 (0.54 to 0.78, −1.5 to 1.8 μm, 0.63 to 0.89), respectively. We identified evidence for re overestimation (underestimation) correlated with shadowing (enhanced reflectance) in the forward (backscattering) hemisphere, and limitations to illumination and viewing configurations accessible by GOES-13, depending on the time of day and season. A proposition is hypothesized to ameliorate 3-D biases by studying relative illumination and cloud spatial inhomogeneity.

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.

Relativistic Electron Precipitation Near Midnight: Drivers, Distribution, and Properties

AbstractWe analyze the drivers, distribution, and properties of the relativistic electron precipitation (REP) detected near midnight by the Polar Orbiting Environmental Satellites (POES) and Meteorological Operational (MetOp) satellites, critical for understanding radiation belt losses and nightside atmospheric energy input. REP is either driven by wave‐particle interactions (isolated precipitation within the outer radiation belt), or current sheet scattering (CSS; precipitation with energy dispersion), or a combination of the two. We evaluate the L‐MLT distribution for the identified REP events in which only one process evidently drove the precipitation (∼10% of the REP near midnight). We show that the two mechanisms coexist and drive precipitation in a broad L‐shell range (4–7). However, wave‐driven REP was also observed at L < 4, whereas CSS‐driven REP was also detected at L > 7. Moreover, we estimate the magnetotail stretching during each REP event using the magnetic field observations from the Geostationary Operational Environmental Satellite (GOES). Both wave‐particle interactions and CSS drive REP in association with a stretched magnetotail, although CSS‐driven REP potentially shows more pronounced stretching. Wave‐driven REP events are localized in L shell and often occur on spatial scales of <0.3 L. Using either proton precipitation (observed by POES/MetOp during wave‐driven REP) as a proxy for electromagnetic ion cyclotron (EMIC) wave activity or wave observations (from GOES and the Van Allen Probes) at the conjugate event location, we find that ∼73% wave‐driven REP events are associated with EMIC waves.

Informing Improvements in Freeze/Thaw State Classification Using Subpixel Temperature

Freeze/thaw (FT) processes at the earth’s surface can have a considerable effect on global carbon, energy, and hydrologic cycles. Therefore, an accurate representation of FT is valuable to adequately monitor and model these processes. In this study, we assess the relationship between satellite-based FT products and modeled surface and soil temperatures over North America. In addition, hourly land surface temperature (LST) from the Geostationary Operational Environmental Satellite (GOES) system is also compared to FT classifications. Utilizing the higher spatial resolution temperatures (~5 km), we assess subgrid-scale variability and its relationship to coarser microwave FT classifications (>25 km). We also examine product agreement and subpixel characteristics across the land cover, climate, and topography. FT classifications are shown to vary widely depending on these variables, leading to an ambiguous definition of frozen and thawed states. Our results suggest that current products can characterize FT transitions with consistent subfreezing surface characteristics in far northern regions (>50 °N). However, uncertainty associated with FT classifications is shown to increase considerably as latitude decreases. Our results also suggest that fractional FT products, utilizing data inputs, such as LST, would provide a considerable improvement in mountainous regions with high intergrid cell heterogeneity, in regions characterized by ephemeral FT events (i.e., regions < 40 °N), as well as during freeze and thaw onset periods. This study also provides insight to improving the representation of surface FT state by providing a clearer definition of the subpixel scale temperature characteristics that govern existing frozen classifications.