The Configurable Reflectarray for Electronic Wideband Scanning Radiometry (CREWSR) is a future microwave imaging and sounding sensor that offers low power, low mass, low cost, high performance, and compatibility with Evolved Expendable Launch Vehicle (EELV) Secondary Payload Adapter (ESPA)-class small satellite systems. CREWSR will demonstrate a highly configurable SmallSat-based microwave temperature sounder on several key capabilities: the spatial resolution, the sampling distance between two nearby fields of view (FOVs), the ability to scan anywhere within its scene to allow angular sampling, the number of spectral channels, and the observation noise. These capabilities will optimize the observing strategy so that more observations can be made in regions that are data sensitive. This work focuses on evaluating three configurable aspects of CREWSR: the spatial resolution, the ground sample distance, and the angular samplings. A quick regional hybrid observing system simulation experiment (OSSE) work was conducted to understand the added value of CREWSR on Hurricane Ian (2022) forecast. Results show that assimilating both CREWSR and ATMS radiances in the early morning orbit improves the Hurricane Ian (2022) forecast on track and intensity. The finer spatial resolution can further improve the forecast. Most importantly, CREWSR radiances with the optimized observing strategy improve the forecast the best. These results demonstrate that a microwave sounder like CREWSR, with the capability to provide more observations in data-sensitive regions and fewer observations in data-insensitive regions, has the potential to further improve hurricane forecast beyond the current sensors' capabilities. SIGNIFICANCE STATEMENT: This study evaluates the potential benefits of the Configurable Reflectarray for Electronic Wideband Scanning Radiometry (CREWSR), a next-generation microwave sounder, for improving hurricane forecasts. Unlike current operational sounders, CREWSR offers on-the-fly configurability in spatial resolution, ground sampling distance, and angular sampling. These capabilities allow for finer-resolution and/or multiangle observations in regions where dense data are most needed, thereby enhancing the information content assimilated into numerical weather prediction models. Results from the Hurricane Ian (2022) case study demonstrate that CREWSR can provide meaningful improvements in both track and intensity forecasts, highlighting its potential to advance satellite-based forecasting of high-impact weather.

Short‐wave infrared (SWIR) radiances around the 4.3 μm CO 2 absorption band from polar‐orbiting hyperspectral sounders provide useful thermodynamic information for numerical weather prediction (NWP) models. They are not assimilated in any of the operational NWP models because the Non‐Local Thermodynamic Equilibrium (NLTE) effects can increase the brightness temperature by more than 10 K. Directly assimilating NLTE‐affected SWIR radiances is challenging because of two reasons: (1) the radiative transfer model like the Community Radiative Transfer Model (CRTM) underestimates NLTE effects by 0.76 K from the old CRTM NLTE coefficients and by 0.46 K from the new CRTM NLTE coefficients, leading to day/night discrepancies in observation minus background (OMB) bias; and (2) CRTM does not simulate aurora‐related NLTE effects, which can happen during day and night. In this study, methodologies are developed to bias correct CRTM NLTE simulations to minimize the day/night discrepancies in OMB biases and to quality control SWIR radiances that cannot be well simulated by CRTM. The NLTE estimates from the Spectral Correlations to Estimate Non‐local Thermal Equilibrium (SCENTE) method — which exhibit better agreement between observations and background than those from CRTM simulation—were used as a reference to develop the linear regression‐based bias correction scheme. Extensive evaluations were carried out to understand the performance of the bias correction and the quality control schemes. Results showed that the schemes reduced day/night discrepancies in OMB bias to less than 0.1 K for different seasons and 0.14 K for different satellite zenith angles. These small discrepancies open the potential to assimilate daytime and nighttime SWIR radiances simultaneously. In addition, the quality control procedure is effective in screening out SWIR radiances affected by aurora‐related NLTE effects. A larger percentage of nighttime data were filtered out compared to daytime, underscoring the importance of addressing nighttime SWIR radiance assimilation. Lastly, the large OMB biases in high latitudes reported in previous studies are eliminated after the bias correction and quality control.

Le service des États-Unis pour la météorologie et l’océanographie, la NOAA, est responsable des satellites météorologiques opérationnels américains. A l’heure actuelle, il opère les satellites en orbite polaire Suomi-NPP, NOAA-20 et NOAA-21, dont les instruments perfectionnés mesurent de nombreux paramètres atmosphériques globaux. Il opère également les satellites géostationnaires GOES-16, -17 et -18 qui fournissent en temps réel des images météorologiques à haute résolution du continent américain.

Monitoring and predicting highly localized weather events over a very short-term period, typically ranging from minutes to a few hours, are very important for decision makers and public action. Nowcasting these events usually relies on radar observations through monitoring and extrapolation. With advanced high-resolution imaging and sounding observations from weather satellites, nowcasting can be enhanced by combining radar, satellite, and other data, while quantitative applications of those data for nowcasting are advanced through using machine learning techniques. Those applications include monitoring the location, impact area, intensity, water vapor, atmospheric instability, precipitation, physical properties, and optical properties of the severe storm at different stages (pre-convection, initiation, development, and decaying), identification of storm types (wind, snow, hail, etc.), and predicting the occurrence and evolution of the storm. Satellite observations can provide information on the environmental characteristics in the preconvection stage and are very useful for situational awareness and storm warning. This paper provides an overview of recent progress on quantitative applications of satellite data in nowcasting and its challenges, and future perspectives are also addressed and discussed.

Geostationary Extended Observations, or GeoXO, is NOAA’s future geostationary satellite constellation, set to launch in the early 2030s and operate into the 2050s. Given changes to the Earth system, improvements in technology, and expanding needs of satellite data users, GeoXO will extend NOAA’s current observation suite by adding three new instruments and one new spacecraft. Improved versions of the imager and lightning mapper will again be placed on East and West satellites, where they will monitor severe storms, tropical cyclones, fires, and other hazards. They will be joined by an ocean color instrument designed for detection of harmful algal blooms, phytoplankton, chlorophyll- a , and other constituents. The third geostationary spacecraft will be placed in the center of the United States and will carry a hyperspectral infrared sounder, an atmospheric composition instrument, and potentially a partner payload. Radiances from the sounder will be assimilated into numerical weather prediction models to improve forecasts, and sounder-derived retrievals of vertical profiles of temperature and water vapor will allow forecasters to detect and track areas of enhanced instability. Retrievals of pollutants such as nitrogen dioxide and ozone from the new atmospheric composition instrument along with trace gas measurements from the sounder will be used to improve air quality monitoring, forecasts, and warnings in addition to climate monitoring. Once complete, the GeoXO constellation will contribute to an international “geo ring” of satellites that will be used for worldwide weather, oceans, climate, and air quality monitoring. This revolutionary new geostationary satellite constellation will provide critical observations for a changing Earth system.

A parallax shift is a displacement in the apparent navigated position of a feature that arises because of its perspective from the viewing platform and is also a function of the feature height. For Geostationary Operational Environmental Satellite (GOES) imagery, this shift is especially apparent away from the satellite subpoint. Users should understand the degree of this shift when combining GOES Advanced Baseline Imager (ABI) imagery with other data, such as radar and lightning. However, it can be challenging, especially at spatial resolutions around the cloud/storm scale. This article explores parallax displacement for both uniform and computed cloud-top heights. Parallax shift will be shown using two case studies. The first case is from 7 September 2021, in which northern Illinois hailstorms are examined using ground-based Level II NEXRAD radar data, GOES-16 ABI imagery, and Geostationary Lightning Mapper data. The second case, on 9 April 2021, examines an eruption of the La Soufrière volcano on St. Vincent from the differing perspectives of GOES16 and -17. The discussion of these cases will show how parallax is an apparent displacement that will vary depending on what satellites are used for observation, where the phenomenon is with respect to the satellite, and the height of the phenomenon being analyzed. Newer satellite instruments with finer spatial resolutions and improved georeferencing will maximize data usability at more extreme angles and require users to account for the accompanying enhanced parallax shift. Even at lesser angles, parallax displacement is an important consideration for many meteorological and other applications.

Over the past few decades, global monitoring of the Earth's environment has improved considerably. Observing and predicting the Earth's environment makes it possible to protect lives and properties and prepare for future impacts. This chapter presents an overview of the United States operational Earth-observing satellite program, led by the National Oceanic and Atmospheric Administration (NOAA). It covers the history of the NOAA program dating back to the 1960s and summarizes the evolution of its capabilities in terms of diversity and quality of the measurements, on both polar-orbiting and geostationary platforms. Two important classes of sensors that will be discussed are imagers and sounders. Both passively measure the Earth System radiation. The chapter discusses the past, present and expected upcoming portfolio of NOAA satellites, as well as the products and services that are or will be derived from the NOAA satellites.

The human eye is sensitive to three primary bands of light-centered on the red, green, and blue parts of the visible spectrum. The human eye is not very sensitive to variations in shades of gray-being able to distinguish only approximately 25 different gradations of gray in satellite images. However, by using the three different color sensors, the eye has the potential to distinguish up to a million different values of color. Hence, color is a powerful tool for distinguishing various objects of interest with subtle intensity variations. The Geostationary Operational Environmental Satellites-R (GOES-R) series of geostationary satellites do not have a green channel. However, a synthetic green channel can be constructed from the blue, red, and nearinfrared '' veggie '' channels for the use in a true-color visible image. Since the launch of the GOES-16 satellite, several different groups have developed color visible algorithms that are available on public websites. The purpose of this paper is to help explain the similarities and differences of true-color GOES images that are on the web and in other locations.

A storm tracking and nowcasting model was developed for the contiguous US (CONUS) by combining observations from the advanced baseline imager (ABI) and numerical weather prediction (NWP) short-range forecast data, along with the precipitation rate from CMORPH (the Climate Prediction Center morphing technique). A random forest based model was adopted by using the maximum precipitation rate as the benchmark for convection intensity, with the location and time of storms optimized by using optical flow (OF) and continuous tracking. Comparative evaluations showed that the optimized models had higher accuracy for severe storms with areas equal to or larger than 5000 km2 over smaller samples, and loweraccuracy for cases smaller than 1000 km2, while models with sample-balancing applied showed higher possibilities of detection (PODs). A typical convective event from August 2019 was presented to illustrate the application of the nowcasting model on local severe storm (LSS) identification and warnings in the pre-convection stage; the model successfully provided warnings with a lead time of 1–2 h before heavy rainfall. Importance score analysis showed that the overall impact from ABI observations was much higher than that from NWP, with the brightness temperature difference between 6.2 and 10.3 microns ranking at the top in terms of feature importance.

Compared with low Earth orbit (LEO) satellites, the high altitude from geostationary Earth orbit (GEO) satellites leads to increased diffraction effects on hyperspectral infrared (HIR) sounders, which reduces the ensquared energy (EE) within the satellites’ field of view (FOV) and increases the pseudo noise of the measurements. To help understand how the instrument performance is affected by EE for the Geostationary Extended Observations Sounder (GXS), a point spread function (PSF) is used to simulate the contribution of each location within and outside of an FOV ( $4\times $ 4 km at nadir). The PSF is applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) airborne simulator (MAS) data with a spatial resolution of 50 m, to determine an appropriate EE for GXS. Although wavenumber dependent, an EE of 70% is recommended, which ensures that all GXS channels have pseudo noise less than the instrument specifications. Regardless of the EE value, the pseudo noise reduces the precision of the temperature and moisture sounding retrievals in the troposphere. Even with an EE of 70%, the pseudo noise slightly increases the root-mean-square error (RMSE) by 3%–4% for temperature and by 1%–4% for relative humidity. If an EE of 70% is difficult to meet, due to cost for example, a lower EE can be a good tradeoff with only a slight degradation in the sounding retrieval quality, which may be overcome with spatial averaging using the inverted cone method. An EE of 50% would lead to an RMSE increase of about 6% for temperature and 3%–6% for relative humidity.

Radiance measurements from a geostationary hyperspectral infrared sounder (GeoHIS) with high temporal resolution not only provide a continuous weather cube of atmospheric temperature and moisture information at different pressure levels, but also enable derivation of three‐dimensional (3D) horizontal winds by tracking atmospheric water vapor features. However, GeoHIS radiances are influenced by sub‐footprint cloudiness, which needs to be considered in tracking the moisture features for deriving the atmospheric wind fields. By combining the collocated high spatial resolution cloud information from an imager onboard the same platform, the 3D horizontal wind retrievals can be improved, and the influence of sub‐footprint cloudiness on winds can be quantified for better applications. Using data from the Advanced Geostationary Radiation Imager (AGRI) and Geostationary Interferometric Infrared Sounder onboard the same experimental geostationary satellite Fengyun‐4A, it is found that 3D horizontal wind retrievals can be derived under both clear and partially clear skies with reasonable accuracy. Sub‐footprint cloud information provides noticeable improvement in wind retrievals; higher/lower clouds have more/less influence while thicker/thinner clouds have more/less influence, respectively, on the wind product. The sub‐footprint cloudiness (cloud‐top pressure and cloud coverage) provides a good indication of the quality flag for quantitative applications of the 3D horizontal wind product.

A quality control (QC) process which handles surface impacts is an important step toward successful assimilation of the Advanced Baseline Imager (ABI) water vapor (WV) band radiances. If the QC is too relaxed, many surface contaminated radiances get assimilated. If the QC is too stringent, useful radiances are rejected. Either way can result in reduced or even compromised observation impacts. A new machine learning‐based QC scheme for the three ABI WV bands is developed and optimized to help understand the importance and effectiveness of the scheme. Unlike previous schemes which are dependent on the background, this scheme extracts and blends the surface information from 7 ABI bands (bands 8–10, 13–16) to determine if a WV radiance is affected by the surface. Simulation studies show that the new QC scheme is effective in retaining radiances that are either unaffected by the surface or have very small surface contamination. It is highly effective in rejecting radiances with large surface contamination. Numerical experiments from a single case study of Hurricane Harvey (2017) were carried out to optimize the QC and to understand the potential impacts on forecasts. The use of the new QC scheme shows that radiances from each WV band have substantially added value. Combining them has a positive impact on hurricane track forecasts compared with existing QC schemes. Hence, it is critical that an optimized QC scheme is used for infrared WV radiance assimilation. It provides a balance between positive impacts from useful radiances and negative impacts from surface contaminated radiances.

NOAA's Geostationary Extended Observations (GeoXO) satellite system will provide advanced hyperspectral resolution infrared observations. The first GeoXO Sounder (GXS) will fly in the mid-2030s and provide an unprecedented level of information. The plans are for the GXS to be located at a longitude over the center of the U.S. The GXS will be as much of an improvement over the legacy Geostationary Operational Environmental Satellite (GOES) broad-spectral resolution sounder as the Advanced Baseline Imager (ABI) was to the legacy imager. The GXS will be the U.S. contribution to the global ring of geostationary advanced infrared sounders. The GXS sensor will provide unique information about the vertical structure of moisture, winds, and temperature to support both Numerical Weather Prediction (NWP) and nowcasting applications.

A hyperspectral infrared (IR) sounder from geostationary orbit provides nearly continuous measurements of atmospheric thermodynamic and dynamic information within a weather cube, specifically the atmospheric temperature, moisture, and wind information at different pressure levels that are critical for improving high-impact weather (HIW) nowcasting and numerical weather prediction (NWP). Geostationary hyperspectral IR sounders (GeoHIS) have been on board China's Fengyun-4 series since 2016 and will be on board Europe's Meteosat Third Generation (MTG) series in the 2024 time frame; the United States and other countries are also planning to include GeoHIS instruments on their next generation of geostationary weather satellites. Although availability of on-orbit GeoHIS data are limited currently, studies have been conducted and progress has been made on developing the applications of high-temporal-resolution GeoHIS observations. These include but are not limited to deriving three-dimensional wind fields for nowcasting and NWP applications, trending atmospheric instability for warning in preconvective environments, conducting impact studies with data from the experimental Geostationary Interferometric Infrared Sounder (GIIRS) on board Fengyun-4A, preparing observing system simulation experiments (OSSEs), and monitoring diurnal variation of atmospheric composition. This paper provides an overview of the current applications of GeoHIS, discusses the data processing challenges, and provides perspectives on future development. The purpose is to provide direction on utilization of the current and assist preparation for the upcoming GeoHIS observations for nowcasting, NWP and other applications.

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.

Abstract Historically, the long wavelength side (6–7.5 μm) of the mid‐infrared water vapor absorption band has been used for imaging from the geostationary perspective. This began with the 6.4 μm band on Europe's Meteosat‐1. While geostationary sounders for moisture profiling, including China's hyperspectral resolution infrared sounder and a planned sounder from Europe, are or will be measuring the short wavelength side of the water vapor band, this is not the case for geostationary imagers. Shorter wavelength (5–6 μm) spectral bands for imaging applications should be considered for observing moisture in the mid and lower troposphere because of several potential advantages offered by this spectral range. The short wavelength side of the water vapor band contains fewer additional absorbing gases that overlap the water vapor absorption lines. In addition, the shorter wavelengths would show less diffraction blurring which could enable finer spatial resolution for turbulence detection. This study considers some of the differences and potential advantages of the spectral information in the short wavelength side of the water vapor absorption band from the perspective of geostationary imaging. For dry conditions land heating could impact the qualitative use of observed brightness temperatures at 5.1 μm, while the solar reflection component over most clear‐sky scenes is small for a 5.1 μm band and essentially zero for a 5.6 μm band.

Severe storms are often associated with high temporal and spatial variations in atmospheric moisture deviation. Satellite‐based hyperspectral IR sounders are widely used for weather forecasting and data assimilation in numerical weather prediction. Current infrared (IR) sounders have spatial resolutions ranging from 12 to 16 km, with future sounders improved to 4–8 km. It is important to understand if measurements from the current and future IR sounders can capture small‐scale atmospheric moisture variations, especially during mesoscale weather events. Using measurements from three Advanced Himawari Imager moisture absorption bands, different sounder resolutions are simulated for sub‐footprint moisture variation analysis. Current sounders are limited when attempting to capture small‐scale moisture variations, especially over land and in the pre‐convection environment. In contrast, future sounders such as the InfraRed Sounder with 4 km resolution can better capture such small‐scale variations. In addition, the higher spatial resolution IR sounders provide more clear sky observations for applications.