Comment on egusphere-2025-5763

2026-03-02

<strong class="journal-contentHeaderColor">Abstract.</strong> Global Navigation Satellite System (GNSS) Radio Occultation (RO) is a vital technique in atmospheric remote sensing, providing all-weather, high-resolution vertical observations that support numerical weather prediction (NWP) and atmospheric research. To enhance understanding of GNSS RO processing uncertainties and inter-algorithm consistency, NOAA/STAR developed an independent RO inversion algorithm based on the Full Spectrum Inversion (FSI) technique to derive bending angle and refractivity profiles from excess phase data. As part of the international Radio Occultation Modeling Experiment (ROMEX), endorsed by the International Radio Occultation Working Group (IROWG), STAR&rsquo;s FSI results were systematically compared with outputs from the community standard Radio Occultation Processing Package (ROPP) and EUMETSAT datasets. Leveraging multi-GNSS RO observations from both commercial and government-funded missions, the study evaluates consistency across processing approaches using the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) as the reference and structural differences against the three-dataset mean for the ROMEX period. Results reveal high overall agreement, while identifying variations linked to the signal-to-noise ratio (SNR) and mission characteristics, providing critical insights for interpreting ROMEX forecast impact studies and improving GNSS RO data assimilation systems.

Processing Multiple GNSS RO Data Using FSI and ROPP: Results from the ROMEX

2025-11-25

Abstract. Global Navigation Satellite System (GNSS) Radio Occultation (RO) is a vital technique in atmospheric remote sensing, providing all-weather, high-resolution vertical observations that support numerical weather prediction (NWP) and atmospheric research. To enhance understanding of GNSS RO processing uncertainties and inter-algorithm consistency, NOAA/STAR developed an independent RO inversion algorithm based on the Full Spectrum Inversion (FSI) technique to derive bending angle and refractivity profiles from excess phase data. As part of the international Radio Occultation Modeling Experiment (ROMEX), endorsed by the International Radio Occultation Working Group (IROWG), STAR’s FSI results were systematically compared with outputs from the community standard Radio Occultation Processing Package (ROPP) and EUMETSAT datasets. Leveraging multi-GNSS RO observations from both commercial and government-funded missions, the study evaluates consistency across processing approaches using the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) as the reference and structural differences against the three-dataset mean for the ROMEX period. Results reveal high overall agreement, while identifying variations linked to the signal-to-noise ratio (SNR) and mission characteristics, providing critical insights for interpreting ROMEX forecast impact studies and improving GNSS RO data assimilation systems.

NO2 validation during the NASA Asia-Air Quality Flight Campaign with an experimental portable DOAS (PDOAS) at Kaohsiung

2025-01-03

This study focuses on retrieving and validating Nitrogen dioxide (NO₂) trace gas vertical column densities (VCDs) using an experimental Portable Differential Optical Absorption Spectroscopy (PDOAS) instrument deployed at Kaohsiung during the National Aeronautics and Space Administration (NASA) Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) field campaign in the spring of 2024. The PDOAS spectral measurements at multiple viewing elevation angles between 1 and 90 deg were processed with the widely recognized QDOAS software package to obtain Differential Slant Column Densities (DSCD) for NO₂ and O₄. Subsequently, the QDOAS results were further analyzed using the Retrieval of Atmospheric Parameters from Spectroscopic Observations using DOAS Instruments (RAPSODI) software to determine the vertical column densities (VCDs) of NO₂. As an initial evaluation of the NO2 VCD retrievals from PDOAS measurements, the PDOAS retrievals are compared with data from the Geostationary Environment Monitoring Spectrometer (GEMS). After excluding measurements contaminated by clouds, 10 effective measurements collected over four days were compared. A high correlation coefficient of 78.93% was found between our PDOAS-retrieved NO₂ VCDs and those from GEMS. This correlation coefficient is comparable to correlations between long-term daily mean NO₂ retrievals from GEMS and in-situ station observations in Kaohsiung. The compact and portable design of PDOAS allows for flexible and mobile air quality measurements. Additionally, the instrument and methodologies developed for PDOAS can be extended beyond NO₂ analysis, making them suitable for examining aerosols and other trace gases as well.

Long short-term memory (LSTM) deep learning-based prediction of sensor performance time series for improved VIIRS reflective solar band calibration

2025-01-10

Past experience from postlaunch calibrations of Suomi-NPP (SNPP), NOAA-20, and NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) indicates the critical need for quick and accurate predictions of sensor optical throughput response changes. For example, the solar diffuser is a critical component of the VIIRS instrument and serves as a reference standard for the on-orbit calibration of VIIRS reflective solar bands (RSBs). However, the solar diffuser can experience degradation over time resulting from various factors, including exposure to solar ultraviolet (UV) radiation, energetic particles, and contaminants in the space environment. The changes in the optical properties of the solar diffuser material can impact the accuracy and stability of VIIRS radiometric calibration. SNPP VIIRS RSB suffered rapid postlaunch optical throughput degradation in the near-infrared (NIR) band gains (inverse of solar-F factor calibration coefficient) due to mirror contamination along the optical path. Given the short-term and long-term VIIRS radiometric calibration update needs and the delays between planning and execution of post-launch calibration updates, it is critical to accurately predict VIIRS sensor response changes in days or weeks ahead. The advancement of recurrent neural network (RNN) machine learning algorithm for time series prediction provides potential means for fast and accurate calibration time series prediction. In this study, long short-term memory (LSTM) RNN model is used to train and predict VIIRS calibration time series such as VIIRS solar diffuser spectral reflectance change time series. LSTM neural networks are a type of RNN architecture designed to model sequential data and capture short- and long-term dependencies and are well-suited for calibration time series forecasting due to their ability to remember information over extended time periods and to handle sequences of varying lengths. The calibration time series of solar diffuser reflectance change from SNPP VIIRS are used as example input sequences and target values to train the LSTM model. The prediction performance is assessed in terms of prediction error as a function of prediction horizons from one to seven days for spectral bands of VIIRS. The relative Root Mean Square Error (RMSE) of seven-day LSTM predictions of spectral degradations of SNPP solar diffuser reflectance is within 0.2%. The suitability of applying LSTM machine learning model for VIIRS calibration time series predictions is discussed.

Evaluation of VIIRS Thermal Emissive Bands Long-Term Calibration Stability and Inter-sensor Consistency Using Radiative Transfer Modeling

Preprints.org · 2024-02-27 · 2 citations

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

Evaluation of VIIRS thermal emissive bands inter-sensor consistency using radiative transfer modeling

2024-08-16

This study evaluates the inter-sensor consistency of the Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12–M16) across three satellites−Suomi National Polar-orbiting Partnership (S-NPP), NOAA-20, and NOAA-21, covering a period from March 18, 2023, to June 30, 2024. The field of interest is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperatures (BTs) using collocated European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. <br/> <br/> Excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O) using CRTM simulation as a radiance transfer. This method relies on the observation minus background BT differences (O-B ΔBTs), between VIIRS measurements obtained from daily operational data (O) and CRTM simulations (B). The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M-band TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.236 K, respectively, which are comparable to the 0.2 K difference observed in overlapping TEBs between VIIRS and MODIS at simultaneous nadir overpasses (SNOs). These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. <br/> <br/> Our study confirms the effectiveness of the RTM-based TEB quality evaluation method in assessing inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing a robust mechanism for assessing inter-sensor consistency. Moreover, it has been noticed that for M12, both the time-series of O-O and O-B BT differences possess the greatest vibrations (i.e., largest standard deviations) compared to other bands, alongside the distinct seasonal variations of O-B BT differences. These observations can be attributed to the fact that M12 radiance is affected by reflected solar radiation during daytime, as M12 operates as a shortwave infrared channel. Furthermore, in this study, we’ve also characterized the spatial distributions of inter-VIIRS BT differences, identifying variations among VIIRS M TEBs.

Comparison of Radio Occultation Bending Angle and Refractivity Processed by Different Inversion Algorithms from Multi-Ro Missions

2024-07-07 · 1 citations

This paper presents the development of an independent algorithm at NOAA/STAR for processing radio occultation (RO) bending angle and refractivity data from RO observations explicitly designed for multi-GNSS RO missions. The primary aim is to understand the uncertainties introduced in the processing, from excess phase data to bending angle and refractivity profiles. This study investigates three algorithms that convert RO excess phases to bending angles. These three algorithms are full spectrum inversion (FSI), canonical transform type 2 (CT2), and phase matching (PM). The STAR-developed FSI algorithm has been fully integrated into the Radio Occultation Processing Package (ROPP) version 10.0. This integration provides users with an alternative to wave optics and geometry optics through configurable settings. A detailed comparison of bending angle and refractivity results generated by these three algorithms, explicitly focusing on COSMIC-2 and Spire data, is presented. The analysis highlights discrepancies and uncertainties inherent in bending angle and refractivity processing, providing valuable insights into the performance of each algorithm.

Evaluation of VIIRS Thermal Emissive Bands Long-Term Calibration Stability and Inter-Sensor Consistency Using Radiative Transfer Modeling

Remote Sensing · 2024-04-04 · 3 citations

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12–M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites (S-NPP, NOAA-20, and NOAA-21) over eight months spanning from 18 March to 30 November 2023. The field of interest is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.102 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily VIIRS operational data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference observed in overlapping TEBs between VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. It is also found that the remnant scene temperature dependence of NOAA-21 versus NOAA-20/S-NPP M13 O-O BT difference after accounting for SRF difference is ~0.0033 K/K, an order of magnitude smaller than the corresponding rates in the direct BT comparisons between NOAA-21 and NOAA-20/S-NPP. Our study confirms the versatility and effectiveness of the RTM-based TEB quality evaluation method in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing a robust mechanism for assessing inter-sensor consistency. Moreover, for M12 operating as a shortwave infrared channel, it is found that the daytime O-B BT differences of S-NPP M12 exhibit greater seasonal variability compared to the nighttime data, which can be attributed to the idea that M12 radiance is affected by the reflected solar radiation during the daytime. Furthermore, in this study, we’ve also characterized the spatial distributions of inter-VIIRS BT differences, identifying variations among VIIRS M TEBs, as well as spatial discrepancies between the daytime and nighttime data.

An in-depth structural and dynamic look at the specific enzymatic characteristics of TREX1 unveils its varied functions in processing both RNA and DNA/RNA hybrids

Acta Crystallographica Section A Foundations and Advances · 2024-08-26

Spire RO Thermal Profiles for Climate Studies: Initial Comparisons of the Measurements from Spire, NOAA-20 ATMS, Radiosonde, and COSMIC-2

Remote Sensing · 2023-07-25 · 17 citations

Global Navigation Satellite System (GNSS) Radio Occultation (RO) data play an essential role in improving numerical weather prediction (NWP) and monitoring climate change. The NOAA Commercial RO Purchase Program (CDP) purchased RO data provided by Spire Global Inc. To ensure the data quality from Spire Global Inc. is consistent with other RO missions, we need to quantify their accuracy and retrieval uncertainty carefully. In this work, Spire Wet Profile (wet temperature profile) data from 7 September 2021 to 31 October 2022, processed by the University Corporation for Atmospheric Research (UCAR), and COSMIC-2 (Constellation Observing System for Meteorology, Ionosphere, and Climate-2/Formosa Satellite Mission 7) data are evaluated through comparison with NOAA-20 Advanced Technology Microwave Sounder (ATMS) microwave sounder measurements and collocated RS41 radiosonde measurements. Through the Community Radiative Transfer Model (CRTM) simulation, we convert the Spire and COSMIC-2 RO retrievals to ATMS brightness temperature (BT) at sounding channels CH07 to CH14 (temperature channels), with weighting function peak heights from 8 km to 35 km, and CH19 to CH22 (water vapor channels), with weighting function peak heights ranging from 3.2 km to 6.7 km, and compare the simulations with the collocated NOAA-20 ATMS measurements over ocean. Using ATMS observations as references, Spire and COSMIC-2 BTs agree well with ATMS within 0.07 K for CH07-14 and 0.20 K for CH19-22. The trends between Spire and COSMIC-2 are consistent within 0.07 K/year over the oceans for ATMS CH07-CH13 and CH19-22, indicating that Spire/COSMIC-2 wet profiles are, in general, compatible with each other over oceans. The RO retrievals and RS41 radiosonde observation (RAOB) comparison shows that above 0.2 km altitude, RS41 RAOB matches Spire/COSMIC-2 temperature profiles well with a temperature difference of &lt;0.13 K, and the trends between Spire and COSMIC-2 are consistent within 0.08 K/year over land, indicating that Spire/COSMIC-2 wet profiles are overall compatible with each other through RS41 RAOB measurements over land. In addition, the consistency of Spire and COSMIC-2 based on different latitude intervals, local times, and signal-to-noise ratios (SNRs) through ATMS was evaluated. The results show that the performance of Spire is comparable to COSMIC-2, even though COSMIC-2 has a higher SNR. The high quality of RO profiles from Spire is expected to improve short- and medium-range global numerical weather predictions and help construct consistent climate temperature records.