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Observations indicate that the spin states of retired and otherwise defunct satellites are diverse and can change significantly over time. Understanding defunct satellite spin state evolution is im...

Derived from satellite measurements, the skin sea surface temperature (SSTHammerDriveskin) is a critical variable in research into the climate and other aspects of the ocean-atmosphere system As the new generation of visible and infrared imaging radiometers, the Advanced Baseline Imager (ABI) onboard the current U.S. geostationary satellites series provides marked improvements to remote sensing of the ocean surface from geostationary orbit, including a wider range of spectral bands and more rapid sampling. This research compares SSTHammerDriveskin derived from the ABI onboard the Geostationary Operational Environmental Satellite (GOES)-16 satellite from January 2018 to October 2019 with the independent data acquired from Marine-Atmospheric Emitted Radiance Interferometers (M-AERIs) deployed on ships. The result shows an averaged SSTHammerDriveskin mean bias of 0.086 K and a robust standard deviation of 0.220 K. The properties of the bias are determined based on various aspects such as geographical distribution, day/night, aerosol effect, and satellite zenith angle. This study also shows that the ABI sensor-specific error statistics (SSES) are effective to reduce the errors by slant-path water vapor and satellite zenith angle. The main result of this research is that the SSTHammerDriveskin products derived from the ABI compare well with independent, International System of Units (SI)-traceable radiometric skin temperatures taken from ships, and this should give confidence in the use of these remotely-sensed SSTHammerDriveskin fields for many scientific endeavors. Some remaining challenges have been identified for algorithm developers to address, and we hope these will be pursued in due course.

Satellite retrievals of cloud droplet effective radius (re) and optical depth (τ) from the Thirteenth Geostationary Operational Environmental Satellite (GOES-13) and the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua and Terra, based on the Clouds and the Earth's Radiant Energy System (CERES) project algorithms, are evaluated with airborne data collected over the midlatitude boundary layer during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). The airborne dataset comprises in situ re from the Cloud Droplet Probe (CDP) and remotely sensed re and τ from the airborne Research Scanning Polarimeter (RSP). GOES-13 and MODIS (Aqua and Terra) re values are systematically greater than those from the CDP and RSP by at least 4.8 (GOES-13) and 1.7 µm (MODIS) despite relatively high linear correlation coefficients (r=0.52–0.68). In contrast, the satellite τ underestimates its RSP counterpart by −3.0, with r=0.76–0.77. Overall, MODIS yields better agreement with airborne data than GOES-13, with biases consistent with those reported for subtropical stratocumulus clouds. While the negative bias in satellite τ is mostly due to the retrievals having been collected in highly heterogeneous cloud scenes, the causes for the positive bias in satellite re, especially for GOES-13, are more complex. Although the high viewing zenith angle (∼65∘) and coarser pixel resolution for GOES-13 could explain a re bias of at least 0.7 µm, the higher GOES-13 re bias relative to that from MODIS is likely rooted in other factors. In this regard, a near-monotonic increase was also observed in GOES-13 re up to 1.0 µm with the satellite scattering angle (Θ) over the angular range 116–165∘; that is, re increases toward the backscattering direction. Understanding the variations of re with Θ will require the combined use of theoretical computations along with intercomparisons of satellite retrievals derived from sensors with dissimilar viewing geometry.

A range of upcoming missions propose to rendezvous in high Earth orbits, including the geostationary orbit region. This region is known to periodically experience high levels of electrostatic charg...

Green bands in satellite remote sensing play an important role in monitoring water and vegetation information. Due to the lack of observed green band, the Geostationary Operational Environmental Satellite (GOES-16) Advanced Baseline Imager (ABI) sensor uses a synthetic one. This study presents an ABI green band generation method using the Pix2Pix based on conditional generative adversarial networks (CGANs) and convolutional neural network techniques with data observed in the visible range of the GOES-16/ABI sensor. Our model was constructed from the radiance data sets in the red, blue, and green bands of the Advanced Himawari Imager (AHI) onboard Himawari-8/9 satellites from August 27, 2018 to May 1, 2019, and applied to generate a GOES-16 ABI green band using the ABI blue band radiance data. A comparison between the AHI and the Pix2Pix-generated AHI green bands displayed high accuracy, evaluated through bias = 0.120, root mean square error (RMSE) = 0.983 in digital number (DN) units, and correlation coefficient (CC) = 0.999. Furthermore, comparison between the Pix2Pix-generated and synthetic ABI green bands resulted in a good agreement (bias = 1.029 and RMSE = 2.892 in DN units, CC = 0.993). The statistical comparison between the green band, and red or blue band resulted in the exceptional performance of the Pix2Pix-generated ABI green band compared to the synthetic ABI green band. Consequently, our Pix2Pix-based model can be effectively used to generate nonexistent green band of ABI sensor and be applied in a variety of scientific applications requiring green band.

The results of a study of the dependence of the dynamics of relativistic electron fluxes of the Earth’s outer radiation belt on the geomagnetic storm intensity are presented. A total of 22 geomagnetic storms with |Dst|max of ∼50 to ∼200 nT are considered. The studies are based on experimental data on fluxes of electrons with an energy of ~2 MeV received from the Van Allen Probe spacecraft in the core of radiation belts and from the GOES satellite in geostationary orbit. It is shown that the dominant influence on the dynamics of relativistic electrons in the core of the Earth’s outer radiation belt during strong magnetic storms with |Dst|max ~ ≥120 nT is exerted by global changes of the magnetospheric magnetic field, which lead to adiabatic variations of fluxes of relativistic electrons.

An ability to accurately detect convective regions is essential for initializing models for short-term precipitation forecasts. Radar data are commonly used to detect convection, but radars that provide high-temporal-resolution data are mostly available over land, and the quality of the data tends to degrade over mountainous regions. On the other hand, geostationary satellite data are available nearly anywhere and in near-real time. Current operational geostationary satellites, the Geostationary Operational Environmental Satellite-16 (GOES-16) and Satellite-17, provide high-spatial- and high-temporal-resolution data but only of cloud top properties; 1 min data, however, allow us to observe convection from visible and infrared data even without vertical information of the convective system. Existing detection algorithms using visible and infrared data look for static features of convective clouds such as overshooting top or lumpy cloud top surface or cloud growth that occurs over periods of 30 min to an hour. This study represents a proof of concept that artificial intelligence (AI) is able, when given high-spatial- and high-temporal-resolution data from GOES-16, to learn physical properties of convective clouds and automate the detection process. A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data. The model takes five temporal images from channel 2 (0.65 µm) and 14 (11.2 µm) as inputs and produces a map of convective regions. In order to provide products comparable to the radar products, it is trained against Multi-Radar Multi-Sensor (MRMS), which is a radar-based product that uses a rather sophisticated method to classify precipitation types. Two channels from GOES-16, each related to cloud optical depth (channel 2) and cloud top height (channel 14), are expected to best represent features of convective clouds: high reflectance, lumpy cloud top surface, and low cloud top temperature. The model has correctly learned those features of convective clouds and resulted in a reasonably low false alarm ratio (FAR) and high probability of detection (POD). However, FAR and POD can vary depending on the threshold, and a proper threshold needs to be chosen based on the purpose.

Spin state predictions for defunct satellites in geosynchronous earth orbit (GEO) are valuable for active debris removal and servicing missions as well as material shedding studies and attitude-dependent solar radiation pressure (SRP) modeling. Previous studies have shown that solar radiation torques can explain the observed spin state evolution of some GEO objects via the Yarkovsky-O'Keefe-Radzievskii-Paddack (YORP) effect. These studies have focused primarily on uniform rotation. Nevertheless, many objects are in non-principal axis rotation (i.e. tumbling). Recent exploration of the tumbling regime for the family of retired GOES 8-12 satellites has shown intriguing YORP-driven behavior including spin-orbit coupling, tumbling cycles, and tumbling period resonances. To better explore and understand the tumbling regime, we develop a semi-analytical tumbling-averaged rotational dynamics model. The derivation requires analytically averaging over the satellite's torque-free rotation, defined by Jacobi elliptic functions. Averaging is facilitated by a second order Fourier series approximation of the facet illumination function. The averaged model is found to capture and explain the general long-term behavior of the full dynamics while reducing computation time by roughly three orders of magnitude. This improved computation efficiency promises to enable rapid exploration of general long-term rotational dynamics for defunct satellites and rocket bodies.

Monitoring leaf phenology tracks the progression of climate change and seasonal variations in a variety of organismal and ecosystem processes. Networks of finite-scale remote sensing, such as the PhenoCam network, provide valuable information on phenological state at high temporal resolution, but they have limited coverage. Satellite-based data with lower temporal resolution have primarily been used to more broadly measure phenology (e.g., 16 d MODIS normalized difference vegetation index (NDVI) product). Recent versions of the Geostationary Operational Environmental Satellites (GOES-16 and GOES-17) can monitor NDVI at temporal scales comparable to that of PhenoCam throughout most of the western hemisphere. Here we begin to examine the current capacity of these new data to measure the phenology of deciduous broadleaf forests for the first 2 full calendar years of data (2018 and 2019) by fitting double-logistic Bayesian models and comparing the transition dates of the start, middle, and end of the season to those obtained from PhenoCam and MODIS 16 d NDVI and enhanced vegetation index (EVI) products. Compared to these MODIS products, GOES was more correlated with PhenoCam at the start and middle of spring but had a larger bias (3.35 ± 0.03 d later than PhenoCam) at the end of spring. Satellite-based autumn transition dates were mostly uncorrelated with those of PhenoCam. PhenoCam data produced significantly more certain (all p values ≤0.013) estimates of all transition dates than any of the satellite sources did. GOES transition date uncertainties were significantly smaller than those of MODIS EVI for all transition dates (all p values ≤0.026), but they were only smaller (based on p value <0.05) than those from MODIS NDVI for the estimates of the beginning and middle of spring. GOES will improve the monitoring of phenology at large spatial coverages and provides real-time indicators of phenological change even when the entire spring transition period occurs within the 16 d resolution of these MODIS products.

This paper presents the tracking and short-term forecasting of mesoscale convective cloud clusters (CCs) that occurred over southeast Brazil and the adjacent Atlantic Ocean during 2009–17. These events produce intense rainfall and severe storms that impact agriculture, defence, hydroelectricity and offshore oil production. To identify, track and forecast CCs, the Geostationary Operational Environmental Satellite infrared imagery and the Forecasting and Tracking the Evolution of Cloud Clusters method are used. The forecast performance is investigated by applying statistical analyses between the observed and forecasted CCs’ physical properties. A total of 7139 mesoscale convective CCs were identified, tracked and selected for the short-term forecasting at their maturation phases. The CC tracking showed a high frequency of CCs over the Atlantic Ocean and mainly over continental and coastal southeast Brazil during the wet season. This indicates an important role played by the cold fronts and convective diurnal forcing on the organisation of convective cloudiness over that region. The majority of the CCs reached their maturation phases within the first 2 h of life cycle, which occurred mostly between the late afternoon and evening. The CCs had short lifetimes and were predominantly in meso-β scales, followed by meso-α convective CCs. The CCs showed cloud-top temperatures typical of clouds with strong vertical development and potential to produce rainfall. The short-term forecasting of CCs at their maturation phases revealed different behaviours of the statistical indices with forecast range. For the 30–60-min timeframe, the forecasts performed relatively well. For longer forecast lead times (90–120 min), the forecasts overestimated the occurrences, intensities and growth of the CCs and forecasted the CCs to be further north and east of their actual observed locations. Overall, our results may contribute to improving the forecast quality of these intense weather events.

Fog is a hazardous weather event that can endanger navigation, aviation, and transportation. While human has several limitations in detecting and forecasting offshore fog, satellite remote sensing offers cost-effective images. In this study, a probability-based daytime sea fog detection algorithm, applied to geostationary operational environmental satellite (GOES) 16 satellite data over the Grand Banks offshore Eastern Canada, is presented and compared with the National Oceanographic and Atmospheric Administration (NOAA)'s Low Instrument Flight Rules (LIFR) probability map. Initially, clear-sky and ice cloud classes were delineated in the GOES-16 image and then the remaining pixels were assigned a fog probability by conducting small droplet proxy, spatial homogeneity, and temperature difference tests. Moreover, a green band was linearly interpolated using the first three bands of GOES-16 images to generate pseudotrue color composites. The resulting maps were evaluated both during an extended sea fog event and using several statistical measures. The average detection probability for the observed advection fog events was 66% for the proposed method, while that for NOAA's LIFR map was 38%. Furthermore, by thresholding the generated maps at the probability of 60%, the false alarm rate, probability of detection, hit rate, and Hanssen–Kuiper skill score were 0.09, 0.77, 0.83, and 0.68, respectively. The proposed method is operationally being used in this region to detect and monitor sea fog, facilitating safe navigation and aviation. This is the first study that uses GOES-16 for daytime fog detection and discusses a satellite-based solution for fog modeling in Grand Banks, NL.

The study of the localized plasma conditions before the impulsive phase of a solar flare can help us understand the physical processes that occur leading up to the main flare energy release. Here, we present evidence of a hot X-ray onset interval of enhanced isothermal plasma temperatures in the range of 10-15~MK up to tens of seconds prior to the flare's impulsive phase. This `hot onset' interval occurs during the initial soft X-ray increase and prior to the detectable hard X-ray emission. The isothermal temperatures, estimated by the Geostationary Operational Environmental Satellite (GOES) X-ray sensor, and confirmed with data from the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI), show no signs of gradual increase, and the `hot onset' phenomenon occurs regardless of flare classification or configuration. In a small sample of four representative flare events we identify this early hot onset soft X-ray emission mainly within footpoint and low-lying loops, rather than with coronal structures, based on images from the Atmospheric Imaging Assembly (AIA). We confirm this via limb occultation of a flaring region. These hot X-ray onsets appear before there is evidence of collisional heating by non-thermal electrons, and hence they challenge the standard flare heating modeling techniques., Comment: Submitted to MNRAS 6 July 2020

The life cycle of clouds consists of mainly into three phases, namely developing, mature, and dissipating phases. The information about the vertical structure of the precipitation during different ...

The paper presents the results of the reconstruction of the effective height h' and the slope of the profile β of the electron concentration in the D layer of the ionosphere during X-ray flares of M- and X-classes at the midlatitudes. The reconstruction was carried out with data from measurements at the observatory of the Institute of Geospheres Dynamics of the Russian Academy of Sciences at the Mikhnevo observatory of amplitudes and the phases of signals from VLF transmitters GQD (19.6 kHz) and GBZ (22.1 kHz) that propagate along one mid-latitude path. Based on the calculations, the empirical dependencies of the parameters h' and β on the flare energy according to the X-ray flux measurements on the GOES satellite are in the range 0.05–0.4 nm. Criteria are proposed for the selection of solar flares for the verification of theoretical computational models.

Smoke forecasts have been challenged by high uncertainty in fire emission estimates. We develop an inverse modeling system, the HYSPLIT-based Emissions Inverse Modeling System for wildfires (or HEIMS-fire), that estimates wildfire emissions from the transport and dispersion of smoke plumes as measured by satellite observations. A cost function quantifies the differences between model predictions and satellite measurements, weighted by their uncertainties. The system then minimizes this cost function by adjusting smoke sources until wildfire smoke emission estimates agree well with satellite observations. Based on HYSPLIT and Geostationary Operational Environmental Satellite (GOES) Aerosol/Smoke Product (GASP), the system resolves smoke source strength as a function of time and vertical level. Using a wildfire event that took place in the southeastern United States during November 2016, we tested the system's performance and its sensitivity to varying configurations of modeling options, including vertical allocation of emissions and spatial and temporal coverage of constraining satellite observations. Compared with currently operational BlueSky emission predictions, emission estimates from this inverse modeling system outperform in both reanalysis (21 out of 21 d; −27 % average root-mean-square-error change) and hindcast modes (29 out of 38 d; −6 % average root-mean-square-error change) compared with satellite observed smoke mass loadings.

As space technologies mature, the cost of inserting materiel into orbit continues to decrease. Advances in the miniaturization of hardware and decreasing launch costs have resulted in space becomin...

The recently launched Geostationary Operational Environmental Satellite “R-series” (GOES-R) satellites carry the Geostationary Lightning Mapper (GLM) that measures from space the total lightning rate in convective storms at high spatial and temporal frequencies. This study assimilates, for the first time, real GLM total lightning data in an ensemble Kalman filter (EnKF) framework. The lightning flash extent density (FED) products at 10-km pixel resolution are assimilated. The capabilities to assimilate GLM FED data are first implemented into the GSI-based EnKF data assimilation (DA) system and tested with a mesoscale convective system (MCS). FED observation operators based on graupel mass or graupel volume are used. The operators are first tuned through sensitivity experiments to determine an optimal multiplying factor to the operator, before being used in FED DA experiments FEDM and FEDV that use the graupel-mass or graupel-volume-based operator, respectively. Their results are compared to a control experiment (CTRL) that does not assimilate any FED data. Overall, both DA experiments outperform CTRL in terms of the analyses and short-term forecasts of FED and composite/3D reflectivity. The assimilation of FED is primarily effective in regions of deep moist convection, which helps improve short-term forecasts of convective threats, including heavy precipitation and lightning. Direct adjustments to graupel mass via observation operator as well as adjustments to other model state variables through flow-dependent ensemble cross covariance within EnKF are shown to work together to generate model-consistent analyses and overall improved forecasts.

The deviation angle variance (DAV) method was developed to objectively estimate tropical cyclone (TC) intensity from geostationary infrared (IR) brightness temperature data. Here, we demonstrate that improvements of 25% root mean square error (RMSE) in major hurricane intensity estimation (relative to best track) can be obtained by considering the pixel-by-pixel satellite view angle in the estimation. Using data from the Chinese Fengyun 2E and 2F satellites for Super Typhoon Soudelor (2015), we demonstrate how the satellite observation angle can reduce the accuracy of intensity estimation, especially for the strongest TCs. Based on these results, an improved DAV estimator is developed using 12 years (2004–2015) of Geostationary Operational Environmental Satellite (GOES)-East satellite IR images over the North Atlantic basin.

The diurnal cycle of tropical cyclones (TCs) is a daily cycle in clouds that appears in satellite images and may have implications for TC structure and intensity. The diurnal pattern can be seen in infrared (IR) satellite imagery as cyclical pulses in the cloud field that propagate radially outward from the center of nearly all Atlantic-basin TCs. These diurnal pulses, a distinguishing characteristic of this diurnal cycle, begin forming in the storm’s inner core near sunset each day, appearing as a region of cooling cloud-top temperatures. The area of cooling takes on a ring-like appearance as cloud-top warming occurs on its inside edge and the cooling moves away from the storm overnight, reaching several hundred kilometers from the circulation center by the following afternoon. The state-of-the-art TC diurnal cycle measurement in IR satellite imagery has a limited ability to analyze the behavior beyond qualitative observations. We present a method for quantifying the TC diurnal cycle using one-dimensional persistent homology, a tool from Topological Data Analysis, by tracking maximum persistence and quantifying the cycle using the discrete Fourier transform. Using Geostationary Operational Environmental Satellite IR imagery from Hurricanes Felix and Ivan, our method is able to detect an approximate daily cycle.

Evolution and morphological properties of the active region NOAA 9087 are analyzed based on space and ground observational data. Data on hard X-ray (HXR) and soft X-ray (SXR) were obtained from Yohkoh Telescopes (HXT and SXT) and the Geostationary Operational Environmental Satellite. Magnetograms and images in far ultraviolet were obtained from the Michelson Doppler Imager (MDI) and Extreme ultraviolet Imaging Telescope (EIT) of the Solar and Heliospheric Observatory (SOHO). White light images from the Big Bear Observatory (BBSO) and Hα-filtergrams from the Meudon Observatory were used. Data on the 2.69 GHz radio flux were taken from the World Data Center of the Learmonth Observatory (Australia). The investigated active region (AR) was observed on the solar disk from July 15 to 27, 2000, and showed a complex multipolar magnetic field configuration. A high flare activity and emissions were observed in this AR. According to Solar Geophysical Data (SGD), the 3N/M6.4 two-ribbon flare occurred on July 19, 2000, and lasted 2.5 h. The energy was released sequentially in different locations in the AR. All observational data indicate a continuous change in the structure and power of the flare at different wavelengths. HXR and type III radio bursts were observed at the initial phase of the flare. The HXR coronal source was above the line of magnetic polarity inversion of the AR. Observational evidence of magnetic reconnections during the main phase of the flare is obtained based on the analysis of sequential images of loops in the ultraviolet wavelength band. Postflare loops were observed in the 19.5 nm passband at the gradual phase, which is a manifestation of the EUV late phase. These extended loops connect the primary and secondary energy release sites of the flare. There was an additional energy transfer and heating mechanism during the main phase of the flare.

Currently, it is possible to access a large amount of satellite weather information from monitoring and forecasting severe storms. However, there are no methods of employing satellite images that can improve real-time early warning systems in different regions of Mexico. The auto-estimator is the most commonly used technique that was developed for specific locations in the United States of America (32°–49° latitude) for the type of convective storms. However, the estimation of precipitation intensities for meteorological conditions in tropic latitudes, using the auto-estimator technique, needs to be re-adjusted and calibrated. It is necessary to improve this type of technique that allows decision-makers to have hydro-informatic tools capable of improving early warning systems in tropical regions (15°–25° Mexican tropic latitude). The main objective of the work is to estimate rainfall from satellite imagery in the infrared (IR) spectrum from the Geostationary Operational Environmental Satellite (GOES), validating these estimates with a network of surface rain gauges. Using the GOES-13 IR images every 15 min and using the auto-estimator, a downscaling of six hurricanes was performed from which surface precipitation events were measured. The two main difficulties were to match the satellite images taken every 15 min with the surface data measured every 10 min and to develop a program in C+ that would allow the systematic analysis of the images. The results of this work allow us to get a new adjustment of coefficients in a new equation of the auto-estimator, valid for rain produced by hurricanes, something that has not been done until now. Although no universal relationship has been found for hurricane rainfall, it is evident that the original formula of the auto-estimator technique needs to be modified according to geographical latitude.

Data from the Geostationary Operational Environmental Satellite 16 (GOES-16) are used to study the number of clear nights over Colombia in order to identify potential sites for astronomical observa...

From 2004 to 2017, an effort was undertaken to integrate space-borne sensing and in situ sensing in an automated system to improve global volcano activity monitoring. This paper reviews a sensor we...

Due to the limitations in the number of satellites and the swath width of satellites (determined by the field of view and height of satellites), it is impossible to monitor global aerosol distribution using polar orbiting satellites at a high frequency. This limits the applicability of aerosol optical depth (AOD) data sets in many fields, such as atmospheric pollutant monitoring and climate change research, where a high-temporal data resolution may be required. Although geostationary satellites have a high–temporal resolution and an extensive observation range, three or more satellites are required to achieve global monitoring of aerosols. In this article, we obtain an hourly and global AOD data set by integrating AOD data sets from four geostationary weather satellites [Geostationary Operational Environmental Satellite (GOES-16), Meteosat Second Generation (MSG-1), MSG-4, and Himawari-8]. The integrated data set will expand the application range beyond the four individual AOD data sets. The integrated geostationary satellite AOD data sets from April to August 2018 were validated using Aerosol Robotic Network (AERONET) data. The data set results were validated against: the mean absolute error, mean bias error, relative mean bias, and root-mean-square error, and values obtained were 0.07, 0.01, 1.08, and 0.11, respectively. The ratio of the error of satellite retrieval within ±( $0.05+ 0.2\times $ AODAERONET) is 0.69. The spatial coverage and accuracy of the MODIS/C61/AOD product released by NASA were also analyzed as a representative of polar orbit satellites. The analysis results show that the integrated AOD data set has similar accuracy to that of the MODIS/AOD data set and has higher temporal resolution and spatial coverage than the MODIS/AOD data set.

The recent successful deployment of the Geostationary Lightning Mapper (GLM) on board the Geostationary Operational Environmental Satellite R series (GOES-16/17) provides nearly uniform spatiotemporal measurements of total lightning (intracloud plus cloud to ground) over the Americas and adjacent vast oceanic regions. This study evaluates the potential value of assimilating GLM-derived water vapor mixing ratio on short-term (≤6 h), cloud-scale (dx = 1.5 km) forecasts of five severe weather events over the Great Plains of the United States using a three-dimensional variational (3DVAR) data assimilation (DA) system. Toward a more systematic assimilation of real GLM data, this study conducted sensitivity tests aimed at evaluating the impact of the horizontal decorrelation length scale, DA cycling frequency, and the time window size for accumulating GLM lightning observations prior to the DA. Forecast statistics aggregated over all five cases suggested that an optimal forecast performance is obtained when lightning measurements are accumulated over a 10-min interval and GLM-derived water vapor mixing ratio values are assimilated every 15 min with a horizontal decorrelation length scale of 3 km. This suggested configuration for the GLM DA together with companion experiments (i) not assimilating any data, (ii) assimilating radar data only, and (iii) assimilating both GLM and radar data were evaluated for the same five cases. Overall, GLM data have shown potential to help improve the short-term (

The 16th Geostationary Operational Environmental Satellite (GOES-16) is the first satellite launched from National Oceanic and Atmospheric Administration’s (NOAA’s) next-generation Geostationary Op...

Land surface emissivity (LSE) is a key parameter for the determination of land surface temperature (LST) from thermal remotely sensed data. A new LSE product has been developed at the National Oceanic and Atmospheric Administration (NOAA), College Park, MD, USA, to enhance the LST product for the Joint Polar Satellite System (JPSS) and the Geostationary Operational Environmental Satellite R-Series (GOES-R) missions as well as to support the forecasting models. A 1-km resolution bare ground emissivity was derived from the historical Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset (ASTER-GED) and Moderate Resolution Imaging Spectroradiometer (MODIS) LSE product. It is then spectrally adjusted to the split window (SW) channels of Visible/Infrared Imager Radiometer Suite (VIIRS) onboard JPSS and Advanced Baseline Imager (ABI) from GOES-R. VIIRS daily green vegetation fraction and snow fraction products are subsequently used to account for the dynamic variations. Validation results indicate that the product has a good agreement with in situ observations, with an emissivity difference less than 0.006 at the bare surface sites and a difference less than 0.007 at one cropland site with three growth stages. The intercomparison with NASA VIIRS daily LSE product proves a good agreement with a standard deviation of less than 0.012. To evaluate the LSE performance in LST retrieval, a whole year VIIRS LST is produced and validated over the SURFace RADiation budget observing network (SURFRAD) sites. The results indicate that the LSE works well with LST RMSE of 1.78 (daytime) and 1.58 K (nighttime). The new LSE algorithm has been integrated to NOAA-20 LST with the status of provisional maturity and will be applied on GOES-16/17 LST product in the near future.

In the second half of the twentieth century, following demands for more accurate environmental monitoring of the Earth’s two satellite systems, geostationary (GOES) and polar-orbiting (POES) were developed to observe the ocean atmosphere and land for more accurate monitoring, modeling and prediction of environmental impacts on economy and human life. The measurements from the GOES satellite were used for weather monitoring and predictions. POES data were used intensively to estimate land cover change, vegetation health and soil moisture, to detect and monitor drought and vegetation stress, to predict fire and malaria risk, to model crop and pasture production and for other purposes affecting the humanity. Most of these POES products are used successfully for prediction of food shortages and the related food security problems. This chapter describes the POES data collection and processing (especially noise removal) from the two NOAA operational polar-orbiting satellite systems (initial – NOAA/AVHRR and new – SNPP/VIIRS) and data preparation for continued applications and predictions in agriculture, human health, food security, climate and land changes, disasters detection (drought, crop losses etc.). Since the late 20th, these data have been used for malaria monitoring.