This study evaluates mission-long inter-sensor radiometric calibration biases in Sensor Data Record (SDR) and/or Temperature Data Record (TDR) radiances from NOAA microwave sounders, including Advanced Technology Microwave Sounder (ATMS) (Suomi National Polar-orbiting Partnership or SNPP, NOAA-20, NOAA-21) and Advanced Microwave Sounding Unit-A (AMSU-A) (NOAA-19). Using four complementary validation techniques within the Inter-Sensor Radiometric Bias Assessment (iSensor-RCBA) system—32-day averaging, Community Radiative Transfer Model (CRTM) Double Difference (DD), Simultaneously Nadir Overpass (SNO), and sensor-DD via SNO—we characterize long-term performance. Results indicate that the SDR/TDR radiance quality remains stable and generally meets scientific requirements throughout their operational lifetimes with minimal anomalies; observed anomalies were infrequent and primarily correlated with calibration-table updates or spacecraft events or instrument degradation. Moreover, this research examines how radiometric calibration biases for the three ATMS instruments vary with Earth scene radiance or temperatures using the CRTM and SNO methods, as well as the radiance-dependency of inter-sensor calibration biases across the three instruments. Notably, due to its exceptional stability over 14 years, despite an approximate two-month data gap, the SNPP ATMS TDR and SDR datasets are recommended as the ideal reference to link legacy AMSU-A and Microwave Humidity Sounder (MHS) with Joint Polar Satellite System (JPSS), QuickSounder, and MetOp-Second Generation (MetOp-SG) microwave instruments. Beyond quantifying data quality, our multi-method framework with iSensor-RCBA effectively diagnosed critical issues, including a simulation error for CRTM ATMS radiance related to the CRTM spectral-response approximation and a NOAA-19 AMSU-A channel-8 performance anomaly. These findings confirm the long-term integrity of NOAA microwave sounder records and reinforce the value of integrated cross-sensor calibration assessments.

This study provides a comprehensive, long-term evaluation of inter-sensor radiometric calibration biases for the NOAA OMPS Nadir and CrIS instruments using four complementary validation methodologies implemented within the Inter-Sensor Radiometric Bias Assessment (iSensor-RCBA) portal, a component of the STAR Integrated Calibration/Validation System. Overall, SDR data quality from the three OMPS Nadir instruments and three CrIS instruments aboard SNPP, NOAA-20, and NOAA-21 remains stable. The iSensor-RCBA portal has also proven to be a powerful diagnostic resource, enabling the detection of both new and previously unrecognized calibration issues and anomalies. Using the 32-day averaged difference method, we were the first to discover and identify the root cause of an inconsistency near 280 nm in inter-sensor radiometric biases between the SNPP and NOAA-20 OMPS NP instruments. The same method also revealed an unusual radiometric feature in NOAA-21 CrIS SDRs over the southern high latitudes during spring and summer. In addition, we derived decade-long degradation rates at 11 Metop-B GOME-2 wavelengths using an independent dataset—Simultaneous Nadir Overpass observations between SNPP OMPS and Metop-B GOME-2. Furthermore, iSensor-RCBA monitoring confirmed two geolocation anomalies in SNPP CrIS through a new approach involving SNO-based inter-sensor biases between GOES-16 ABI and SNPP CrIS. These cases demonstrate that iSensor-RCBA is not only a monitoring visualization tool but also a diagnostic tool that delivers unique, complementary insight into instrument performance, enabling early identification of radiometric and geolocation issues across JPSS and other satellite missions. Importantly, the analysis methods used in this study are broadly applicable to current and future missions, including JPSS-03, JPSS-04, and non-NOAA satellite systems.

The Ozone Mapping and Profiler Suite-Nadir Mapper (OMPS-NM) is a key satellite instrument for retrieving and analyzing ozone concentrations in both the total column and different atmospheric layers. Given the relatively large footprint of the OMPS-NM sensor, accurately detecting cloud-contaminated fields of view (FOVs) at the sub-pixel scale is essential for both calibration/validation efforts and the direct assimilation of Sensor Data Record (SDR) radiances into numerical weather prediction (NWP) models. To address this challenge, a novel sub-pixel cloud detection model has been developed using deep learning techniques. The model is trained using reference cloud information from the Visible Infrared Imaging Radiometer Suite (VIIRS), which is collocated on the same satellite platform as OMPS-NM. The training dataset consists of globally matched NOAA-21 OMPS-NM and VIIRS measurements which are randomly selected from different months in 2024 to capture a wide range of atmospheric and surface conditions. For each OMPS-NM footprint, the collocated VIIRS cloud mask is used as the ground truth to train a deep neural network on the corresponding OMPS-NM spectral signatures. To reduce model complexity and improve efficiency, the OMPS-NM radiance spectra are transformed into principal components (PCs), with only the top 14 PCs used as input features. The model’s performance is evaluated globally against the VIIRS-derived cloud mask. Results show a strong spatial agreement between the model predictions and VIIRS cloud data, achieving a high global mean detection accuracy of 89%. Regionally, the model performs slightly better over oceans (90%) than over land (87%). Further comparison with the operational OMPS-NM effective cloud fraction, derived from NOAA’s total ozone product, demonstrates that the deep learning model is significantly more effective at identifying sub-pixel clouds that are often missed by the operational method. This enhanced detection capability highlights the model’s potential for improving the accuracy and reliability of OMPS-NM science products1.

The Ozone Mapping and Profiler Suite (OMPS) and the Geostationary Environment Monitoring Spectrometer (GEMS) are satellite instruments designed to monitor atmospheric composition, primarily ozone. OMPS onboard SNPP/NOAA-20/NOAA-21 satellites are polar orbiting instruments, providing global coverage once a day. However, GEMS, onboard South Korea's GEO-KOMPSAT-2B satellite, is a geostationary satellite instrument providing more frequent observations over East Asia and adjacent areas. This difference in their orbits defines their unique capabilities and limitations, making intercomparison an essential exercise for harmonizing their datasets and validating their respective ozone retrievals. OMPS provides global coverage, delivering ozone profiles and trace gases with a lower temporal resolution but comprehensive spatial coverage. GEMS complements this by offering high temporal resolution data, allowing the monitoring of diurnal variations in ozone. The intercomparison between OMPS and GEMS helps to evaluate radiometric calibration consistency of measurements. This study focuses on quantifying the radiometric consistency between NOAA-21 OMPS Nadir Mapper (NM) radiance and reflectance measurements with GEMS. One of the benefits of GEO-LEO intercomparison is the satellites have nearly frequent simultaneous overlapping observations. The sensor intercomparison is performed near nadir for OMPS and GEMS, less than 8 degrees view zenith angle. The radiometric bias results provide a good understanding of how well NOAA-21 OMPS agree with GEMS. The results could help to provide independent validation of OMPS data quality. The study could be further expanded to perform intercomparison of GEMS with SNPP and NOAA-20, resulting in opportunities to evaluate the OMPS radiometric consistency through double difference. This could help to further confirm the SNPP, NOAA-20 and, NOAA-21 OMPS radiometric agreements from the past studies performed using 32-day global average and using deep convective clouds. The results presented in this study are valuable for user communities to better understand how well the OMPS and GEMS agree with each other.

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been continuously providing global environmental data records (EDRs) for more than one decade since its launch in 2011. Recently, the VIIRS EDRs of cloud features have been reprocessed using unified and consistent algorithm for selected periods to minimize or remove the inconsistencies due to different versions of retrieval algorithms as well as input VIIRS sensor data records (SDRs) adopted by different periods of operational EDRs. This study conducts the first simultaneous quality and accuracy assessment of reprocessed Cloud Top Height (CTH) and Cloud Base Height (CBH) products against both the operational VIIRS EDRs and corresponding cloud height measurements from the active sensors of NASA’s CloudSat-CALIPSO system. In general, the reprocessed CTH and CBH EDRs show strong similarities and correlations with CloudSat-CALIPSOs, with coefficients of determination (R2) reaching 0.82 and 0.77, respectively. Additionally, the reprocessed VIIRS cloud height products demonstrate significant improvements in retrieving high-altitude clouds and in sensitivity to cloud height dynamics. It outperforms the operational product in capturing very high CTHs exceeding 15 km and exhibits CBH probability patterns more closely aligned with CloudSat-CALIPSO measurements. This preliminary assessment enhances data applicability of remote sensing products for atmospheric and climate research, allowing for more accurate cloud measurements and advancing environmental monitoring efforts.

This study presents a long-term assessment of inter-sensor radiometric calibration biases for NOAA OMPS nadir and CrIS instruments using four well-established validation methodologies implemented through the Inter-Sensor Radiometric Bias Assessment (iSensor-RCBA) portal, a component of the STAR Integrated Calibration/Validation System (ICVS) monitoring system. Four validation methods include the 32-Day Average, CRTM-DD, SNO, and Sensor-DD via SNO—to enhance monitoring and detect radiometric errors. The results demonstrate that the SDR data quality from three OMPS nadir instruments and three CrIS instruments aboard the SNPP, NOAA-20, and NOAA-21 satellites has generally remained stable over the long term, meeting scientific requirements with some margin—mainly during early orbit phases, anomalies, malfunctions, or calibration updates. Among four methodologies, the 32-Day method excels in identifying limitations of other used validation methods, particularly in terms of inter-sensor bias geographical coverage. For instance, the 32-Day method identifies an unusual feature in the NOAA-21 CrIS SDR data over the high latitudes of the Southern Hemisphere during the spring and summer seasons, which was not detected using the other three methods due to a limited coverage. The SNO method is particularly effective for detecting long-term calibration discrepancies in a single instrument. This is illustrated by an approximately 10-year time series of inter-sensor bias between SNPP OMPS Nadir Mapper and Metop-B GOME-2, which reveals significant degradation in GOME-2. Using the SNO method, two significant geolocation problems occurred on SNPP spacecraft were captured in inter-sensor biases between SNPP CrIS and GOES-16 ABI. Therefore, the iSensor-RCBA portal can serve as a crucial tool for providing supplemental information about long-term radiometric calibration stability of satellite radiance data across JPSS and other satellite instruments.

The Cross-track Infrared Sounder (CrIS) radiance data plays a crucial role in numerical weather prediction (NWP) models by providing essential atmospheric sounding information through data assimilation. However, challenges arise in handling subpixel cloud contamination within CrIS fields of view (FOVs), which can impact the accuracy of radiance simulations. To address this, the Visible Infrared Imaging Radiometer Suite (VIIRS) Radiances Cluster analysis within the CrIS FOVs is developed to characterize subpixel scene homogeneity. This paper describes the algorithms and data processing procedures for this cluster analysis. A fast and accurate collocation method was developed to directly align VIIRS measurements within CrIS FOVs using line-of-sight (LOS) pointing vectors. This method supports both terrain-corrected and non-terrain-corrected VIIRS geolocation data sets as inputs. The K-means clustering method is used to group collocated VIIRS radiance within CrIS FOVs into seven (7) clusters based on their radiance values. The mean, standard deviation, and coverage of each cluster are output for each CrIS FOV. Comparisons with the Infrared Atmospheric Sounding Interferometer cluster analysis demonstrate similar performance, confirming the validity of the CrIS-VIIRS approach. Data assimilation experiments at the European Centre for Medium-Range Weather Forecasts indicate that the VIIRS radiance cluster data can be effectively integrated into NWP models, aiding in cloud detection and improving data quality. These findings highlight the potential of CrIS-VIIRS clustering for enhancing data thinning, quality control, and assimilation of cloudy radiance observations in operational NWP systems. Plain Language Summary To support data assimilations of numerical weather predictions models, the Visible Infrared Imaging Radiometer Suite Radiances Cluster analysis within the Cross-track Infrared Sounder Fields of Views is developed to characterize subpixel scene homogeneity. This paper describes the underlying algorithms, data processing methods, and potential applications.

This study presents our first discovery about two abnormal problems in the blackbody calibration target associated with the antenna unit A2 in the Metop-C AMSU-A instrument. The problems include the anomalous patterns in both blackbody kinetic temperature Tw and radiative temperature (measured in warm count or Cw), and the time lag between orbital cycles of Tw and Cw. This study further determines solar intrusion as the root cause of the anomalous pattern problem. According to our analysis, solar illumination is constantly observed during each orbit near the satellite terminator, causing anomalous changes in Cw and Tw, characterized by sudden and abnormal increases typically for more than 16 min. The resultant maximum antenna temperature errors due to abnormal increases in Cw are approximately in the range from 0.15 K to 0.25 K, while the maximum errors due to the abnormal increase in Tw are in the range from 0.04 K to 0.07 K, varying with orbit, season, and channel. The time shift feature is characterized with a changeable time lag with the season in the Tw orbital cycle in comparison with the Cw cycle. The longest time lag up to about 18 min occurs in summer through early fall, while the time lag can be decreased down to about 9 min in winter through early spring. Hence, this study underscores the imperative need for future research to rectify radiance errors and reconstruct a more accurate long-term Metop-C AMSU-A radiance data set for channels 1 and 2, crucial for climate studies.

The Ozone Mapping and Profiler Suites (OMPS) Nadir Mapper (NM) is a grating spectrometer within the OMPS nadir instruments onboard the SNPP, NOAA-20, and NOAA-21 satellites. It is designed to measure Earth radiance and solar irradiance spectra in wavelengths from 300 nm to 380 nm for operational retrievals of the nadir total column ozone. This study presents calibration and validation analysis results for the NOAA-21 OMPS NM SDR data to meet the JPSS scientific requirements. The NOAA-21 OMPS SDR calibration derives updates of several previous OMPS algorithms, including the dark current correction algorithm, one-time wavelength registration from ground to on-orbit, daily intra-orbit wavelength shift correction, and stray light correction. Additionally, this study derives an empirical scale factor to remove 2.2% of systematic biases in solar flux data, which were caused by pre-launch solar calibration errors of the OMPS nadir instruments. The validation of the NOAA-21 OMPS SDR data is conducted using various methods. For example, the 32-day average method and radiative transfer model are employed to estimate inter-sensor radiometric calibration differences from either the SNPP or NOAA-20 data. The quality of the NOAA-21 OMPS NM SDR data is largely consistent with that of the SNPP and NOAA-20 OMPS data, with differences generally within ±2%. This meets the scientific requirements, except for some deviations mainly in the dichroic range between 300 nm and 303 nm. The deep convective cloud target approach is used to monitor the stability of NOAA-21 OMPS reflectance above 330 nm, showing a variation of 0.5% over the observed period. Data from the NOAA-21 VIIRS M1 band are used to estimate OMPS NM data geolocation errors, revealing that along-track errors can reach up to 3 km, while cross-track errors are generally within ±1 km.

The NOAA-21 satellite successfully completed its post-launch checkout, verification, and validation of all the sensor data and key performance parameters. It is now the primary satellite in NOAA's Joint Polar Satellite System with redundant back up provided by NOAA/NASA Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. The performance of all NOAA-21 data meets specifications and are within family compared to SNPP and NOAA-20. The global data provided by the mission is critical for numerical weather prediction models for making timely and accurate weather forecasts, as well as for detecting and monitoring environmental events such as floods, fires, and changes in atmospheric chemistry such as ozone concentration.

The advanced technology microwave sounder (ATMS) is an important satellite instrument that provides vital data on atmosphere temperature and moisture for weather forecasting and climate research, and helps us plan for extreme weather. However, its coarse resolution and angular dependence have long been a challenge for improving image visualization. This article proposes a method to enhance the imagery visualization for ATMS, combining limb correction (LC) with artificial intelligence (AI) resolution enhancement (RE). Measurement data from the ATMS onboard NOAA-20 were utilized to train the LC method, which were then validated using newly acquired NOAA-21 ATMS data. The AI RE was performed using enhanced super-resolution generative adversarial networks, which increased the pixel resolution by a factor of four. The high-resolution (HR) Advanced Microwave Scanning Radiometer 2 data served as a reference to initially and quantitatively evaluate the RE method. The combined method of LC and AI RE produced an angular-dependence-free and HR ATMS image, resulting in a significant improvement in image visualization, including surface and atmosphere information, and allows for clear identification of severe weather events. For the swift identification and analysis of tropical cyclones in the upcoming season, as of this writing, this proposed method has been routinely employed to produce high-quality global ATMS images, and these images are showcased and tested in the NOAA internal HR imagery visualization system-JSTAR Mapper. Moreover, concentrated efforts are being made to further enhance these images in preparation for an official release.

Over ten-years, the Integrated Calibration and Validation System (ICVS) Long-Term Monitoring (LTM) System has provided near-real time (NRT) monitoring for Joint Polar Satellite System (JPSS) spacecraft and instruments including their on-orbit status and performance and science data product quality [1] - [4]. The ICVS also harnesses JPSS Sensor Data Record (SDR) data to rapidly (with little latency) visualize radiometric features of severe weather events such as hurricanes and volcanos [5] [6]. This study presents two case studies, one depicting the 3-dimensional (3D) atmospheric warm core structure inside Hurricane Ian from the 2022 North Atlantic Hurricane Season and another showing the 3D temperature structures present during the 2021 Heat Dome event by using JPSS ATMS (and VIIRS for hurricane events) SDR and TDR data. More details and images/animations for hurricane events can be found at https://www.star.nesdis.noaa.gov/smcd/sew/index.php.

The Joint Polar Satellite System (JPSS) mission has provided over ten years of high-quality data products for environment forecasting and monitoring through the current Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites. Particularly, the sensor data record (SDR) and the derived environmental data record (EDR) products from the Visible Infrared Imaging Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), the Advanced Technology Microwave Sounder (ATMS), and the Ozone Mapping and Profiler Suite (OMPS) offer an unprecedented opportunity to observe severe weather and environmental events over the Earth. This paper presents the observations about atmospheric features of the Hunga Tonga Volcanic eruption of January 2022, e.g., the gravity wave, volcanic cloud, and aerosol (sulfate) plume phenomena, by using the ATMS, CrIS, OMPS, and VIIRS SDR and EDR products. Powerful gravity waves ringing through the atmosphere after the eruption of the Hunga Tonga volcano are discovered at two CrIS upper sounding channels (670 cm−1 and 2320 cm−1) in the deviations of the observed brightness temperature (O) from the simulated baseline brightness temperature (B) using the Community Radiative Transfer Model (CRTM), i.e., O—B. A similar pattern is also observed in the ATMS global maps at channel 15, whose peak weighting function is around 40 km, showing the atmospheric disturbance caused by the eruption that reached 40 km above the surface. The Tonga volcanic cloud (plume) was also captured by the OMPS SO2 EDR product. The gravity wave features were also captured in the native resolution image of the S-NPP VIIRS I-5 band nighttime observations. In addition, the VIIRS Aerosol Optical Depth (AOD) captured and tracked the volcanic aerosol (sulfate) plume successfully. These discoveries demonstrate the scientific potential of the JPSS SDR and EDR products in monitoring and tracking the eruption of the Hunga Tonga volcano and its severe environmental impacts. This paper presents the atmospheric features of the Hunga Tonga volcano eruption that is uniquely captured by all four advanced sensors onboard JPSS satellites, with different spectral coverages and spatial resolutions.

Among the monitored telemetry raw data record (RDR) parameters with the STAR Integrated/Validation System (ICVS), the Advanced Technology Microwave Sounder (ATMS) scan motor mechanism temperature is especially important because the instrument might be unavoidably damaged if the mechanism temperature exceeds 50 °C. In the current operational flight processing software, the instrument automatically enters safe mode and stops collecting scientific data whenever the mechanism temperature exceeds 40 °C. This approach inevitably leads to the instrument entering safe mode unnecessarily at a premature time, causing the loss of scientific data before the mechanism temperature reaches 50 °C. This study seeks to leverage the influence the main motor current, compensation motor current, and main motor loop integral error have on mechanism temperature to forecast the maximum mechanism temperature over the upcoming 6 min. A long short-term memory (LSTM) neural network predicts maximum mechanism temperature using ATMS RDR telemetry data as the input. The performance of the LSTM is compared with observed maximum mechanism temperatures by applying the LSTM coefficients to several cases. In all cases studied, the mean average error (MAE) of the forecast remained under 1.1 °C, and the correlation between forecasts and measurements remained above 0.96. These forecasts of maximum mechanism temperature are expected to be able to provide information on when the ATMS instrument should enter safe mode without needlessly losing valuable data for the ATMS flight operational team.

Onboard both the Suomi National Polar-orbiting Partnership and Joint Polar Satellite System (JPSS) series of satellites, the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) is a new generation of a total ozone column sensor and is used to generate total column ozone products. This study presents a method for efficiently assessing OMPS-NM geolocation accuracy using spatially collocated radiance measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) Moderate Band M1 by taking advantage of its high spatial resolution (750 m at nadir) and accurate geolocation. The basic idea is to find the best collocation position with maximum correlation between VIIRS collocated and real OMPS-NM radiances by perturbing OMPS-NM line-of-sight (LOS) vectors in the cross-track and along-track directions with small steps in the spacecraft coordinate. The perturbation angles at the best collocation position where OMPS-NM and VIIRS are optimally aligned are used to characterize OMPS-NM geolocation accuracy. In addition, the assessment results can be used to optimize the OMPS-NM field view angle lookup table in the Sensor Data Record (SDR) processing software to improve its geolocation accuracy. To demonstrate the methodology, the proposed method is successfully employed to evaluate OMPS-NM geolocation accuracy with different spatial resolutions. The results indicate that, after the view angle table was updated, the geolocation accuracy for both SNPP and NOAA-20 OMPS-NM is on the sub-pixel level (less than ¼ pixel size) along all the scan positions in both cross-track and along-track directions and the performance is very stable with time. The method proposed in this study lays down the framework for assessing the geolocation accuracy of future high-resolution OMPS-NM measurements.

Earth's surface reflectance is an important parameter affecting ultraviolet (UV) and visible (VIS) radiance calculations at the top of the atmosphere because many UV and VIS channels can acquire information about the surface and atmosphere. This article provides the theoretical basis for deriving the surface reflectance from satellite-measured UV and VIS observations at window and lower sounding channels with the help of the community radiative transfer model (CRTM) and collocated atmospheric profiles such as ozone, water vapor, and aerosols. Cirrus cloud may be included in the calculation as long as the observations contain enough reflected radiation from the surface. An explicit equation with three scalar parameters $\alpha $ , $\beta $ , and $\delta $ is obtained for users to calculate Lambertian surface reflectance from the observation. The expressions for the three parameters are somewhat complicated and computationally expansive. We found a simple and smart way that can exactly calculate the three parameters with quasi-linear functions. Numerical experiments using the CRTM simulations have demonstrated the algorithm accuracy for the surface reflectance retrieval better than 2.0E-14. As a case study, measured surface reflectance and the derived surface reflectance over desert from satellite UV measurements are compared. The derived surface reflectance from Suomi National Polar-orbiting Partnership. Visible Infrared Imaging Radiometer Suite (VIIRS) observations and the VIIRS reflectance product are compared as well. In addition, this methodology can also be used to calculate microwave and infrared surface emissivity with scatterings and solar radiation by adding the surface Planck radiance at the surface temperature.

This article introduces a method to correct intersensor calibration convolution errors that occur in the convolution of spectral response functions (SRFs) between narrow-band and broad-band instruments. By using the intersensor calibration analysis between Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper (NM) and Global Ozone Monitoring Experiment-2 (GOME-2) as an example, the root cause of convolution errors in the intersensor calibration is addressed through direct comparison of OMPS NM SRF and convolved OMPS SRF with GOME-2 SRF. The results reveal that distorted SRF of the narrow-band instrument is the major cause, which appears for GOME-2 at a wide range of channels. The convolution errors in reflectance, which were ignored in previous studies, can be greater than 2% for wavelength shorter than 320 nm and $\sim 0.5$ % for wavelengths between 320 and 330 nm. This study thus presents a hybrid convolution error correction method that consists of theoretical approximation of the convolution errors and empirical estimates of residuals due to the deviation of the theoretical approximation from the actual convolution errors. According to the validation through simulation, after applying convolution error correction, the mean convolution errors are less than 0.02%, while the root mean square errors are reduced from more than 0.5% to less than 0.1%. In addition, the correction method is applied to the intersensor calibration radiometric bias assessment between the Meteorological Operational satellite–B (Metop-B) GOME-2 and the Suomi National Polar-orbiting Partnership (S-NPP) OMPS NM. The averaged intersensor calibration reflectance differences are decreased by more than 16% after convolution error correction.

The Ozone Mapping and Profiler Suite (OMPS) measures ozone concentration in the Earth atmosphere. There are two OMPS units currently flying on board the Suomi NPP and NOAA-20 spacecrafts, respectively. OMPS Sensor Data Records provide users with Earth view radiances from Earth science observation and Solar irradiance via solar observations. OMPS solar observations provide time-dependent measurements of the solar flux over mission times. They also provide information on wavelength variations over the sensors' lifetimes. The solar observations are made through two diffusers at the telescope entrance aperture, a reflective working diffuser for short-term monitoring and a reflective reference diffuser for long-term monitoring of sensor stability. Data collected from the solar observation are used to improve the quality of ozone and other products and maintain the sensor data calibration quality over sensor lifetime. Routine solar calibration adjustments have been conducted for the two Nadir Profiler (NP) sensors in a timeline with the solar measurements via their own solar diffusers. The calibration minimizes the wavelength scale error variations to less than $\pm 0.01\text{nm}$ . A recent improvement will be made to correct for instrument optical degradation which was determined through the changes in the instruments' throughput. These may be as large as to 2.2% for Suomi-NPP NP, and 1.2% for NOAA-20 NP over their current lifetimes.

The Nadir Mapper (NM) and Nadir Profiler (NP) within the Ozone Mapping and Profiler Suites (OMPS) are ultraviolet spectrometers to measure Earth radiance and Solar irradiance spectra from 300–380 nm and 250–310 nm, respectively. The OMPS NM and NP instruments flying on the Suomi-NPP (SNPP) satellite have provided over ten years of operational Sensor Data Records (SDRs) data sets to support a variety of OMPS Environmental Data Record (EDR) applications. However, the discrepancies of quality remain in the operational OMPS SDR data prior to 28 June 2021 due to changes in calibration algorithms associated with the calibration coefficient look-up tables (LUTs) during this period. In this study, we present results for the newly (v2) reprocessed SNPP OMPS NM and NP SDR data prior to 30 June 2021, which uses consistent calibration tables with improved accuracy. Compared with a previous (v1) reprocessing, this new reprocessing includes the improvements associated with the following updated tables or error correction: an updated stray light correction table for the NM, an off-nadir geolocation error correction for the NM, an artificial offset error correction in the NM dark processing code, and biweekly solar wavelength LUTs for the NP. This study further analyzes the impact of each improvement on the quality of the OMPS SDR data by taking advantage of the existing OMPS SDR calibration/validation studies. Finally, this study compares the v2 reprocessed OMPS data sets with the operational and the v1 reprocessed data sets. The results demonstrate that the new reprocessing significantly improves the accuracy and consistency of the life-time SNPP OMPS NM and NP SDR data sets. It also advances the uniformity of the data over the dichroic range from 300 to 310 nm between the NM and NP. The normalized radiance differences at the same wavelength between the NM and NP observations are reduced from 0.001 order (v1 reprocessing) or 0.01 order (operational processing) to 0.001 order or smaller. The v2 reprocessed data are archived in the NOAA CLASS data center with the same format as the operational data.

The Suomi National Polar-orbiting Partnership (SNPP) cross-track infrared sounder (CrIS) has provided critical observations for environmental applications for nearly ten years. However, on 26 March 2019, the Joint Polar Satellite System (JPSS) interface data processing segment (IDPS) stopped producing the operational SNPP CrIS sensor data record (SDR) product due to a failure of the midwave infrared (MWIR) band. Following a comprehensive risk assessment, the switch from primary Side-1 to redundant Side-2 electronics was made on 24 June 2019, successfully recovering the full capabilities of the sensor. Comprehensive assessment results demonstrate the high quality of the CrIS SDR product resulting from the sensor recalibration, thus meeting the JPSS Level-1 requirements with margin. The spectral calibration prioritized consistency with the CrIS SDR product prior to the side switch to minimize the impact on users. The results show that the radiometric impact on the CrIS SDR product resulting from the side switch is not significant and is within the calibration radiometric uncertainty. It is demonstrated that after instrument restoration, the SNPP CrIS SDR product recovers the quality needed to be used as radiometric reference for calibration and validation of infrared remote sensing instruments. The recovery of the SNPP CrIS MWIR band is expected to support improvements in numerical weather forecasting by restoring the MWIR band channels sensitive to tropospheric water vapor. This should also help maintain continuity and redundancy of one of the backbone observations of the global observing system.