This paper provides the latest updates of the NOAA-21 post-launch product validation status. The new products and enhanced capabilities have been transitioned to operations. The status and plans of the reprocessing data records from more than a decade of the Suomi National Polar-orbiting Partnership (SNPP) observations is also provided.

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 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 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.

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 NOAA-20 Ozone Mapping and Profiler Suite (OMPS) is the second OMPS flight unit in the US Joint Polar Satellite System (JPSS) program [1]. Flying on the NOAA-20 satellite, the OMPS extends the 40+-year total column ozone and ozone profile records to monitor ozone concentration in the Earth atmosphere. Recent studies have been conducted over a wide range of radiometric calibration and geolocation calibration areas to improve the quality of OMPS sensor data records. This study covers several topics, including the removal of a solar intrusion signal during science data collection, reduction of solar activity impact resulting from solar activities, update of geolocation accuracy via refinement of the instrument CCD spatial registration, and minimization of a misalignment at the edges of the sensor spatial field of view during Earth view measurements. The objective of this study is to improve OMPS instrument performance to OMPS users' expectations, and to advance the OMPS calibration accuracy of sensor data records above current product requirements [2], [3]. The solar intrusion correction improved local radiance retrieval accuracy 4% for the Nadir Profiler sensor, during science observations in the Northern hemisphere where solar zenith angle ranges from$60^{\circ}$to$88^{\circ}$. Solar model improvements achieve up to 1% better fidelity in irradiance measurements during solar observations. The calibrated CCD spatial registration minimizes the misalignment in the Nadir Mapper sensor spatial field of view in both cross track and along track measurements, and refines the geolocation accuracy to less than 5 km for the sensor's geolocation products that have a nominal spatial resolution of 50 km x 17 km.

The Ozone Mapping and Profiler Suite (OMPS) Nadir Profilers (NPs) are advanced backscatter ultraviolet (BUV) hyperspectral instruments that measure ozone profiles in the Earth atmosphere. The first NP sensor onboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite began its science observations on January 26, 2012, after its aperture door opened. The second OMPS NP, flying on the NOAA-20 satellite, opened its aperture door on January 8, 2018, starting science its data collection. The two NP sensors acquire Earth spectral images along their satellite flight path with a 16.7° wide swath, enabling weekly coverage of vertical ozone distribution in the Earth atmosphere. A successful thorough sensor calibration enables the NP sensors’ data records (SDRs) to meet measurement accuracy requirements. The largest error term in the albedo calibration came from the spectral wavelength calibration. This article provides SDRs accuracy analysis for both NP sensors and discusses important aspects of the SDRs performance in relation to the sensors’ characterization and calibration.

In this article, we first present a generalized methodology for deriving sensor brightness temperature sensor data records (SDR) from antenna temperature data records (TDR) applicable for Advanced Microwave Sounding Unit-A (AMSU-A) instruments. It includes corrections for antenna sidelobe contributions, antenna emission, and radiation perturbation due to the difference of Earth radiance in the main beam and that in the sidelobes that lie outside the main beam but within the Earth disk. For practical purposes, we simplify the methodology by neglecting the components other than the antenna sidelobe contributions to establish a consistent formulation with the legacy AMSU-A antenna pattern correction (APC) formula. The simplified formulation is then applied to the final AMSU-A instrument onboard the Metop-C satellite that was launched in November 2018, in order to compute APC coefficients for deriving SDR from TDR data. Furthermore, the performance of the calculated correction coefficients is validated by calculating the differences between the daily averaged AMSU-A (TDR and SDR) observations against radiative transfer model (O-B) simulations under clear sky conditions, and over open oceans. The validation results show that the derived temperature corrections are channel and scan position dependent, and can add 0.2-1.6 K to the antenna temperatures. In addition, the derived SDR O-B results exhibit a reduced and more symmetric scan angle-dependent bias when compared with corresponding TDR antenna temperatures.

A few deficiencies remain in the current National Oceanic and Atmospheric Administration (NOAA) Integrated Calibration/Validation System (ICVS) noise calculation method for characterizing noise equivalent differential temperature$(NE\Delta T)$performance of on-orbit Advanced Microwave Sounding Unit-A (AMSU-A) instruments. This ICVS method only accounts for the noise contribution resulting from one calibration parameter (warm count). The calculation is also dependent upon an assumption that the calibration gain equals to the sensitivity of warm count to temperature. In addition, the dependence of scene temperature is not considered. This study establishes a new methodology by accounting for the noise components resulting from all calibration parameters such as warm counts, warm target temperatures, space view (cold) counts, and their covariance. Each noise component is computed using the product of the overlapping Allan deviation of corresponding parameter and the sensitivity of Earth scene temperature to the parameter. The new method also comprises the variation of scene temperature via scene count in noise estimation. For the AMSU-A instruments aboard the NOAA-18 to NOAA-19 and Metop-A to Metop-C satellites, the magnitudes of the orbital average$NE\Delta T$calculated using the ICVS method exceed those from the new method by approximately 8%–38%, corresponding to a range from 0.02 to 0.07 K. Particularly, the deviations at the upper temperature sounding channels from 10 to 14 are around 0.05 K. The magnitude of AMSU-A instrument noise can vary by around 18% for sounding channels and 40% for window channels due to scene temperature change. Therefore, the new method demonstrates its significant improvements in characterizing on-orbit AMSU-A instrument noise more accurately and comprehensively.

To provide near-real time monitoring of Suomi-National Polar-orbiting Partnership (S-NPP) and NOAA-20 data inter-sensor biases, this study extends the Geosynchronous Equatorial Orbit - Low-Earth Orbit (GEO-LEO) intercalibration method established in [1] to the Visible Infrared Imaging Radiometer Suite (VIIRS) Sensor Data Record (SDR) data at six reflective solar bands (RSBs) and three thermal emissive bands (TEBs) bands via the STAR Integrated Calibration and Validation System (ICVS) framework. The Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) is used as a transfer to calculate double difference (DD) of VIIRS-ABI Simultaneous Nadir Overpass (SNO) pairs for the S-NPP and NOAA-20 VIIRS SDR cross-calibration biases. A series of sensitivity analyses on the dependence of the results to view geometries, spectral difference corrections, latitudinal variation, and cloud screening are conducted for more accurate bias estimations. The findings are further verified by the other independent approaches, namely the 32-day average difference method (32Day-AD) [2] and DD with radiative transfer model as a transfer (RTM-DD) method.

The Nadir Mapper (NM) is one of two nadir sensors of the Ozone Mapping and Profiler Suite (OMPS) that are designed to measure the ultraviolet radiance backscattered by the Earth's atmosphere and surface as well as solar irradiance. This study assesses the lifetime performance of Suomi National Polar-orbiting Partnership (SNPP) satellite NM reflectance data since its launch by using Simultaneous Nadir Overpass (SNO) collocated observations with Global Ozone Monitoring Experiment-2 (GOME-2) spectrometer onboard Meteorological Operational-B (Metop-B) satellite. The study also analyzes the consistency of the NM data quality between SNPP and NOAA-20 satellite using GOME-2 as a transfer.

A NOAA satellite snowfall rate product is being produced operationally at near real-time. The product is retrieved using measurements from passive microwave radiometers aboard ten polar-orbiting satellites. The algorithm consists of a statistical snowfall detection model and a 1DVAR-based snowfall rate estimation component. The product has benefited from continuous development. Recent advances in bias correction technique have significantly improved the product performance. A validation study was conducted against a radar and gauge combined precipitation dataset recently. The results show that the snowfall rate product agrees well with the validation `truth', e.g. correlation coefficients for all satellites are around 0.6 with low bias and RMS. Some case studies also demonstrate the ability of the algorithm at capturing snowfall pattern and intensity. Currently, this product is being applied in weather forecasting and hydrology.

The performance of on-orbit satellite microwave instrument noise equivalent differential temperature (NEDT) and Temperature Data Record (TDR) data is always critical for the broad user community in both environmental data record (EDR) product retrieval and numerical weather prediction applications. Based on intensive pre-launch and post-launch calibration studies [1]-[5], this study assesses the post-launch performance of Metop-C AMSU-A instrument noise and antenna temperature data since the launch. It also conducts the quality assessment of the AMSU-A data in comparison with radiative transfer model (RTM) simulations, legacy NOAA-18/19 and Metop-A/B AMSU-A observations, and NOAA-20 ATMS observations. Finally, this study addresses impact of polarization and wavelength inconsistency on AMSU-A antenna temperature differences against ATMS.

In this work, the current performance of the calibrated Joint Polar Satellite System (JPSS) Cross-track Infrared Sensor (CrIS) observations is reported. The CrIS instrument is currently on-board the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 spacecraft, and planned for the JPSS-2, -3 and -4 satellites. Presently, calibrated and validated CrIS observations, in the form of sensor data record (SDR) products, are being assimilated by operational NWP models and atmospheric retrieval systems. CrIS measurements from SNPP and NOAA-20 are expected to improve our understanding of the dynamics of the atmosphere due to the higher temporal and spatial coverage resulting from optimally blending the hyperspectral Earth observations. This work also reports recent improvements performed on the CrIS SDR products, including: 1) the implementation of the polarization correction, 2) the optimization of the spike detection and correction algorithm, and 3) the optimization of the lunar intrusion algorithm.

A 1-D variational system has been developed to process spaceborne measurements. It is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. One of the particularities of the system is its applicability in cloudy and precipitating conditions. Although valid, in principle, for all sensors for which the radiative transfer model applies, it has only been tested for passive microwave sensors to date. The Microwave Integrated Retrieval System (MiRS) inverts the radiative transfer equation by finding radiometrically appropriate profiles of temperature, moisture, liquid cloud, and hydrometeors, as well as the surface emissivity spectrum and skin temperature. The inclusion of the emissivity spectrum in the state vector makes the system applicable globally, with the only differences between land, ocean, sea ice, and snow backgrounds residing in the covariance matrix chosen to spectrally constrain the emissivity. Similarly, the inclusion of the cloud and hydrometeor parameters within the inverted state vector makes the assimilation/inversion of cloudy and rainy radiances possible, and therefore, it provides an all-weather capability to the system. Furthermore, MiRS is highly flexible, and it could be used as a retrieval tool (independent of numerical weather prediction) or as an assimilation system when combined with a forecast field used as a first guess and/or background. In the MiRS, the fundamental products are inverted first and then are interpreted into secondary or derived products such as sea ice concentration, snow water equivalent (based on the retrieved emissivity) rainfall rate, total precipitable water, integrated cloud liquid amount, and ice water path (based on the retrieved atmospheric and hydrometeor products). The MiRS system was implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 for the NOAA-18 satellite. Since then, it has been extended to run for NOAA-19, Metop-A, and DMSP-F16 and F18 SSMI/S. This paper gives an overview of the system and presents brief results of the assessment effort for all fundamental and derived products.

Passive microwave sea-ice retrieval algorithms are typically tuned to brightness temperature measurements with simple treatments of weather effects. The new technique presented is a two-step algorithm that variationally retrieves surface emissivities from microwave remote sensing observations, followed by the retrieval of sea-ice concentration from surface emissivities. Surface emissivity spectra are interpreted for determining sea-ice fraction by comparison with a catalog of sea-ice emissivities to find the closest match. This catalog was computed off-line from known ocean, first-year, and multiyear sea-ice reference emissivities for a range of fractions. The technique was adjusted for application to the Advanced Microwave Sounding Unit (AMSU)/Microwave Humidity Sensor observations, and its performance was compared to the National Oceanic and Atmospheric Administration (NOAA)'s AMSU heritage sea-ice algorithm and to NOAA's operational Interactive Multi-sensor Snow and Ice Mapping System taken as ground truth. Assessment results indicate a performance that is superior to the heritage algorithm particularly over multiyear ice and during the warm season.

A microwave land emissivity library has been developed from the Advanced Microwave Sounding Unit (AMSU) data for improving satellite data assimilation. Over the desert, surface emissivity is classified according to soil type into several spectra. For sand, loamy sand, and sandy loam, which contain some large mineral particles, the emissivity spectra generally decrease with frequency. For other desert types whose compositions are dominated by mineral particles smaller than a few hundred micrometers, the emissivity values are almost constant or slightly increasing with frequency. These emissivity features are consistent with those from the land emissivity data set developed at Météo-France. Moreover, both the emissivity library and the Météo-France data set are applied to the assimilation of the AMSU-A data in the National Centers for Environmental Prediction Global Forecast System (GFS). In comparison with the microwave land emissivity model previously developed by Weng , both the emissivity library and the Météo-France data set improve the utilization of the AMSU-A data in the GFS. The increased use of the AMSU-A data through the emissivity library or the data set results in positive impacts on the global medium-range forecasts over either the Southern or Northern Hemispheres.

A 1-D variational retrieval of surface emissivity is developed and applied for the Advanced Microwave Sounding Unit modules A and B, along with the High-resolution Infrared Radiation Sounder. This algorithm offers simultaneous retrieval of infrared and microwave emissivity and increases the separation of the emissivity and land surface temperature signals. The initial estimate of the emissivity for the surface-sensitive channels is made by a combination of physical and empirical microwave emissivity models and, in the infrared, by indexing laboratory measurements to vegetation databases. It is found that the initial estimates of emissivity for snow-free vegetated land areas are within 1% of the retrieved infrared values and, in the microwave, within 4% for all snow-free points and within 2% for the vast majority. The emissivity for snow-covered and sea-ice areas remains problematic, and further investigation is required. It is also shown that the average zenith angle dependence of the emissivity is less than 0.0024 for infrared wavelengths and less than 0.0055 for microwave frequencies if the viewing zenith angles greater than 40 are neglected.

The F16 satellite was successfully launched on October 18, 2003, carrying the first special sensor microwave imager/sounder (SSMIS) onboard. In this paper, the SSMIS imaging channels 12-18 are intercalibated against the F15 special sensor microwave/imager (SSM/I) instrument using simultaneous conical overpassing (SCO) observations from both satellites. Results show that the SSMIS antenna temperatures have a mean bias as large as 1-2 K with a maximum of 3 K at 22.235 GHz with respect to F15. It appears that the mean biases at frequencies from 19.35 to 37 GHz do not strongly depend on the region and season, although the biases at the 91.655-GHz channels are slightly variable. The intercalibration analysis also shows that the nonlinearity may be one of the major sources resulting in differences between F15 SSM/I and F16 SSMIS measurements. For improved calibration and for the future SSM/I and SSMIS reprocessing, the SCO data are further utilized to resolve the SSMIS and SSM/I nonlinearity terms using a newly developed calibration algorithm. With the derived nonlinearity correction, the mean biases of the antenna temperatures between F15 and F16 are significantly reduced. To intercalibrate SSMIS to the same reference as SSM/I, SSMIS imaging channels can also be linearly mapped to the same and similar F15 SSM/I channels using the SCO matchup data. After the linear mapping, SSMIS snow-free land, snow, and sea ice surface emissivities are consistent with those derived from SSM/I.