With this paper, we aim to demonstrate how stratospheric HNO3 can be retrieved from nadir hyperspectral infrared (IR) measurements such that it is largely uncorrelated with tropospheric HNO3 and most other interfering signals. This is achieved by decomposing the set of HNO3 sensitive channels into orthogonal vectors that isolate the stratospheric HNO3 signal for use in the retrieval. Such a nadir-IR HNO3 product could add useful information to the monitoring of some stratospheric chemical processes affecting ozone in the extratropics, especially once the state-of-the-art Microwave Limb Sounder (MLS) on Aura is decommissioned in 2026. Nitric acid is typically used as indicator species for heterogeneous chemical processing inside the winter polar stratospheric vortices. The proposed stand-alone stratospheric HNO3 retrieval would be an improvement over the only other nadir-IR HNO3 product available today, namely FORLI (Fast Optimal Retrieval on Layers for IASI), which is a stratosphere + troposphere correlated profile retrieval affected by uncertainty in tropospheric water vapor at the time of measurement. We demonstrate the potential of this new stratospheric nadir-IR HNO3 retrieval strategy using the Community Long-term Infrared Microwave Combined Atmospheric Processing System (CLIMCAPS) as the retrieval framework with measurements from CrIS (Cross-track Infrared Sounder) on the Joint Polar Satellite System 1 (JPSS-1) during the Northern Hemisphere winter of 2019/2020. Future work will focus on optimizing and validating CLIMCAPS HNO3 retrievals for operational deployment.

The Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) characterizes the atmospheric state as vertical profiles of temperature, water vapor, CO2, CO, CH4, O3, HNO3 and N2O, together with a suite of Earth surface and cloud properties. The CLIMCAPS record spans more than 2 decades (2002–present) because it utilizes measurements from a series of hyperspectral infrared sounders on different satellite platforms. In this paper, we take a stepwise approach to diagnosing CLIMCAPS V2 with the goal of identifying which Bayesian retrieval components to improve for a future V3 release. CLIMCAPS is based on the NASA (National Aeronautics and Space Administration) heritage retrieval approach and is the first system to extend the Aqua record with sounders on next-generation platforms in the same orbit. With the baseline quality of CLIMCAPS V2 soundings well-established, the objective of a V3 upgrade is to improve retrieval consistency across the different instruments and platforms for the sake of a seamless global record of atmospheric soundings. We demonstrate how the retrieval averaging kernels (AKs) are key metrics in diagnosing a multi-instrument system such as CLIMCAPS, and we conclude with the recommendation to upgrade the channel subsets and radiative transfer model error spectrum used in defining the Bayesian measurement error covariance matrix.

The assimilation of data from hyperspectral infrared sounders in global data assimilation systems has historically been focused on observations in the longwave infrared (LWIR) region of the spectrum (650-1100 cm21) despite the often concurrent availability of measurements from the shortwave infrared (SWIR) region of the spectrum (2150-2550 cm21) because issues (like solar effects) have generally prevented the assimilation of SWIR observations. Recent advances in radiative transfer models have worked to address some of the previous challenges in simulating SWIR observations, and the assimilation of SWIR data (e.g., from potential future small-satellites) is now a feasible prospect. Still, a better understanding of how these observations perform in a data assimilation system and impact resulting analyses and NWP forecasts is necessary. In this study, the value of SWIR observations in global NWP is assessed by assimilating SWIR observations from the Cross- track Infrared Sounder (CrIS) in National Oceanic and Atmospheric Administration (NOAA)'s Global Data Assimilation System (GDAS). The methodologies used to enable the assimilation of these observations, including the implementation of a scene-dependent observation error and the enhancement of quality control procedures, are discussed, as are the results of observing system experiments (OSEs) conducted to evaluate the impact of assimilating SWIR observations on forecast skill. The overall results show that SWIR assimilation produces similar forecast impacts to LWIR assimilation. The ability to demonstrate that the assimilation or SWIR observations in NWP is a realistic prospect may help shape future constellations of small satellites to serve as a beneficial complement to the current constellation if hyperspectral infrared (IR) sounders.

Abstract. The Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) characterizes the atmospheric state as vertical profiles (commonly known as soundings or retrievals) of temperature, water vapor, CO2, CO, CH4, O3, HNO3 and N2O, together with a suite of Earth surface and cloud properties. The CLIMCAPS record spans more than two decades (2002–present) because it utilizes measurements from a series of different instruments on different satellite platforms. Most notably, these are AIRS+AMSU (Atmospheric Infrared Sounder + Advanced Microwave Sounding Unit) on Aqua and CrIS+ATMS (Cross-track Infrared Sounder + Advanced Thermal Microwave Sounder) on SNPP and the JPSS series. Both instrument suites are on satellite platforms in low-Earth orbit with local overpass times of ~1:30 am/pm. The CrIS interferometers are identical across the different platforms, but differ from AIRS, which is a grating spectrometer. At first order, CrIS+ATMS and AIRS+AMSU are similar enough to allow a continuous CLIMCAPS record, which was first released in 2020 as Version 2 (V2). In this paper, we take a closer look at CLIMCAPS V2 soundings from AIRS+AMSU (on Aqua) and CrIS+ATMS (on SNPP) to diagnose product continuity across the two instrument suites. We demonstrate how averaging kernels, as signal-to-noise ratio (SNR) indicators, can be used to understand and improve multi-instrument systems such as CLIMCAPS. We conclude with recommendations for future CLIMCAPS upgrades.

Radiance observations from Earth-observing satellites have a significant positive impact on numerical weather prediction (NWP) forecasts, but some spectral regions are not fully exploited. Observations from hyperspectral infrared (IR) sounders in the longwave region (650-1100 cm21), for instance, are routinely assimilated in many NWP models, but observations in the shortwave region (2155-2550 cm21) are not. Each of these regions provides information on the temperature structure of the atmosphere, but the shortwave IR (SWIR) region is considered challenging to assimilate due to noise-equivalent delta temperature (NEDT) that is highly variable depending on scene brightness temperature and to phenomena that are diffi- cult to model, like nonlocal thermodynamic equilibrium (NLTE) and solar reflectance. With recent advances in small-satellite technology, SWIR temperature sounders may provide an agile and cost-effective complement to the current constellation of IR sounders. Therefore, a better understanding of the use and impact of SWIR observations in data assimilation for NWP is warranted. In Part I of this study, as presented here, the amount of unique information [as determined by empirical orthogonal decomposition (EOD)] made available to a data assimilation system by Cross-track Infrared Sounder (CrIS) SWIR observations is reviewed, recent advancements to the Community Radiative Transfer Model (CRTM) for the simulation of CrIS shortwave radiances are tested, and enhancements to NOAA's Global Data Assimilation System (GDAS) for the assimilation of CrIS SWIR observations are implemented and evaluated. Part II of this study, which seeks to assess the value of assimilating shortwave IR observations in global NWP, is also introduced.

The Cross-track Infrared Sounder (CrIS), in low Earth orbit since 2011, makes measurements of the top of atmosphere radiance for input into data assimilation (DA) systems as well as the retrieval of geophysical state variables. CrIS measurements have 2211 narrow infrared channels ranging between 650 and 2550 cm−1 (~3.9–15.4 μm) and capture the variation in profiles of atmospheric temperature, water vapor, and numerous trace gas species. DA systems derive atmospheric temperature by assimilating CO2-sensitive channels in the CrIS longwave (LW) band (650–1095 cm−1). Here, we investigate if CO2-sensitive channels in the shortwave (SW) band (2155–2550 cm−1) can similarly be applied. We first evaluated the information content of the CrIS bands followed by an assessment of the performance degradation of retrievals due to the loss of individual CrIS bands. We found that temperature profile retrievals derived from the CrIS SW band were statistically both well-behaved and as accurate as a retrieval utilizing the CrIS LW band. The one caveat, however, is that the higher CrIS instrument noise in the SW band limited its performance under certain conditions. We conclude with a discussion on the implications our results have for channel selection in retrieval and DA systems as well as the design of future space instruments.

Satellite passive infrared/microwave (IR/MW) sounders have become an indispensable tool for weather forecasting, both in terms of numerical model assimilation and in providing real-time soundings for operational forecasters and nowcasting applications. While IR/MW sounders were originally designed with the weather forecasting application in mind, thermal IR Earth emission spectra contain a plenitude of additional information about atmospheric state parameters. This chapter overviews the development, validation, and application of satellite sounder retrieval systems for atmospheric monitoring and weather forecasting. The success of IR/MW sounder-retrieved products in operational applications depends strongly on knowledge of their accuracy and precision, which are determined using statistical intercomparison methods and accurate in situ field measurements, especially radiosondes, but also ozonesondes and other instrumentation discussed in earlier chapters of this book. In both assimilation and retrieval systems, these field data are needed for the forward problem (i.e., forward radiance calculations), the inverse problem (i.e., the geophysical retrievals, which require training and/or tuning data), and last, but not least, the validation problem (i.e., the error characterization of the end-to-end sounding system). Knowledge of the three-dimensional nature and complexity of atmospheric processes across space and time advances only through exploiting many available instruments and observing systems.

Tropical cyclones can form over open ocean where in situ observations are limited, so forecasters rely on satellite observations to monitor their development and track. We explore the utility of an operational satellite sounding product for tropical forecasting by characterizing the products retrieval skill during research flights. Scientists from both the NOAA-Unique Combined Atmospheric Processing System (NUCAPS) research team and tropical cyclone communities collaborated to target relevant tropical cyclones during the campaign. This effort produced 130 dropsondes that are well-timed with satellite sounder overpasses over three different tropical cyclones and one Saharan Air Layer outbreak. For the combined infrared and microwave retrieval, the NUCAPS temperature has a root mean square error (RMSE) of 1.2 K near the surface (1000–600 mb) and 0.8 K in the mid-troposphere (600–300 mb), which is in line with global product requirements. The water vapor mixing ratio RMSE was 26% near the surface and 46% in the mid-troposphere. NUCAPS microwave-only retrievals can also be useful for cloudy scenes, with surface RMSE values of 4 K (temperature) and 23% (water vapor). Using information content analysis, we estimated that the vertical resolution near the surface was 1.7 km for the temperature retrievals and 2.2 km for the water vapor retrievals in this study. We discuss the feasibility of implementing NUCAPS in an operational forecasting setting, which requires rapid data delivery to forecaster software tools.

Infrared and microwave sounder measurements from polar-orbiting satellites are used to retrieve profiles of temperature, water vapor, and trace gases utilizing a suite of algorithms called the National Oceanic and Atmospheric Administration (NOAA) Unique Combined Atmospheric Processing System (NUCAPS). Meteorologists operationally use the retrievals similar to radiosonde measurements to assess atmospheric stability and aid them in issuing forecasts and severe weather warnings. Measurements of trace gases by NUCAPS enable detection, tracking, and monitoring of greenhouse gases and emissions from fires that impact air quality. During the polar winters, when ultraviolet measurements of ozone are not possible, absorption features in the infrared spectrum of the sounders enable the assessment of ozone concentration in the stratosphere. These retrievals are used as inputs to monitor the ozone hole over Antarctica. This article illustrates the utility of NUCAPS atmospheric profile retrievals in assessing meteorological events using several examples of severe thunderstorms, tropical cyclones, fires, and ozone maps.

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.

M.A. Mischna1, T. Pagano1, G. Liuzzi2, O. Reale2,4, T. Fauchez2,4, S. Desouza-Machado3, G. Martínez4, G. Asrar4, C. Barnet5, 1Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr. Pasadena, CA, 91109, michael.a.mischna@jpl.nasa.gov, 2NASA Goddard Space Flight Center, 3University of Maryland, Baltimore County, 4Universities Space Research Association, 5Science and Technology Corporation

The Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieves vertical profiles of temperature, water vapor, greenhouse and pollutant gases, and cloud properties from measurements made by infrared and microwave instruments on polar-orbiting satellites. These are AIRS/AMSU on Aqua and CrIS/ATMS on Suomi NPP and NOAA20; together they span nearly 2 decades of daily observations (2002 to present) that can help characterize diurnal and seasonal atmospheric processes from different time periods or regions across the globe. While the measurements are consistent, their information content varies due to uncertainty stemming from (i) the observing system (e.g., instrument type and noise, choice of inversion method, algorithmic implementation, and assumptions) and (ii) localized conditions (e.g., presence of clouds, rate of temperature change with pressure, amount of water vapor, and surface type). CLIMCAPS quantifies, propagates, and reports all known sources of uncertainty as thoroughly as possible so that its retrieval products have value in climate science and applications. In this paper we characterize the CLIMCAPS version 2.0 system and diagnose its observing capability (ability to retrieve information accurately and consistently over time and space) for seven atmospheric variables – temperature, H2O, CO, O3, CO2, HNO3, and CH4 – from two satellite platforms, Aqua and NOAA20. We illustrate how CLIMCAPS observing capability varies spatially, from scene to scene, and latitudinally across the globe. We conclude with a discussion of how CLIMCAPS uncertainty metrics can be used in diagnosing its retrievals to promote understanding of the observing system and the atmosphere it measures.

Cold Air Aloft (CAA) can impact commercial flights when cold air descends below 12,192 m (40,000 ft) and temperatures drop dramatically. A CAA event is identified when air temperature falls below −65 °C, which decreases fuel efficiency and poses a safety hazard. This manuscript assesses the performance of the National Oceanic and Atmospheric Administration Unique Combined Atmospheric Processing System (NUCAPS) in detecting CAA events using sounders on polar-orbiting satellites. We compare NUCAPS air temperature profiles with those from Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) for January–March 2018. Of 1311 collocated profiles, 236 detected CAA. Our results showed that NUCAPS correctly detects CAA in 48.1% of profiles, while 17.2% are false positives and 34.7% are false negatives. To identify the reason for these detection states, we used a logistic regression trained on NUCAPS diagnostic parameters. We found that cloud cover can impact the skill even at higher vertical levels. This work indicates that a CAA-specific quality flag is feasible and may be useful to help forecasters to diagnose NUCAPS in real-time. Furthermore, the inclusion of an additional sounder data source (e.g., NOAA-20) may increase CAA forecast accuracy. Cloud scenes change rapidly, so additional observations provide more opportunities for correct detection.

In this paper, we describe how researchers and weather forecasters work together to make satellite sounding data sets more useful in severe weather forecasting applications through participation in National Oceanic and Atmospheric Administration (NOAA)’s Hazardous Weather Testbed (HWT) and JPSS Proving Ground and Risk Reduction (PGRR) program. The HWT provides a forum for collaboration to improve products ahead of widespread operational deployment. We found that the utilization of the NOAA-Unique Combined Atmospheric Processing System (NUCAPS) soundings was improved when the product developer and forecaster directly communicated to overcome misunderstandings and to refine user requirements. Here we share our adaptive strategy for (1) assessing when and where NUCAPS soundings improved operational forecasts by using real, convective case studies and (2) working to increase NUCAPS utilization by improving existing products through direct, face-to-face interaction. Our goal is to discuss the lessons we learned and to share both our successes and challenges working with the weather forecasting community in designing, refining, and promoting novel products. We foresee that our experience in the NUCAPS product development life cycle may be relevant to other communities who can then build on these strategies to transition their products from research to operations (and operations back to research) within the satellite meteorological community.

The Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieves multiple Essential Climate Variables (ECV) about the vertical atmosphere from hyperspectral infrared measurements made by the Atmospheric InfraRed Sounder (AIRS, 2002–present) and its successor, the Cross-track Infrared Sounder (CrIS, 2011–present). CLIMCAPS ECVs are profiles of temperature and water vapor, column amounts of greenhouse gases (CO2, CH4), ozone (O3) and precursor gases (CO, SO2) as well as cloud properties. AIRS (and CrIS) spectral measurements are highly correlated signals of many atmospheric state variables. CLIMCAPS inverts an AIRS (and CrIS) measurement into a set of discrete ECVs by employing a sequential Bayesian approach in which scene-dependent uncertainty is rigorously propagated. This not only linearizes the inversion problem but explicitly accounts for spectral interference from other state variables so that the correlation among ECVs (and their uncertainty) may be minimized. Here, we outline the CLIMCAPS retrieval methodology with specific focus given to its sequential scene-dependent uncertainty propagation system. We conclude by demonstrating continuity in two CLIMCAPS ECVs across AIRS and CrIS so that a long-term data record may be generated to study the feedback cycles characterizing our climate system.