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As a vital parameter describing the Earth surface energy budget, surface all-wave net radiation ($R_{n}$) drives many physical and biological processes. Remote estimation of $R_{n}$ using satellite data is an effective approach to monitor the spatial and temporal dynamics of $R_{n}$. Accurate daily $R_{n}$ estimation typically depends on the spatio-temporal resolutions of satellite data. There are currently few high-spatial-resolution daily $R_{n}$ products from polar-orbiting satellite data, and they exhibit limited accuracy due to sparse diurnal observations. In addition, traditional estimation approaches typically require cloud mask and clear-sky albedo as inputs and ignore the length ratio of daytime (LRD), which may lead to large errors. To overcome these challenges and obtain $R_{n}$ data with improved spatial resolution and accuracy, an operational approach was proposed in this study to derive daily 1-km $R_{n}$ , which takes the advantages from a radiative transfer model, a machine learning algorithm, and multispectral and dense diurnal temporal information of geostationary satellite observations. An improved all-sky hybrid model (AHM) coupling radiative transfer simulations with a random forest (RF) model was first developed to estimate the shortwave net radiation ($R_{ns}$). Then, another RF model was developed to estimate the daily $R_{n}$ from $R_{ns}$ , incorporating the LRD, which is called extended hybrid model (EHM). Data from the Advanced Baseline Imager (ABI) onboard the new-generation Geostationary Operational Environmental Satellite (GOES)-16 with a 5-min temporal resolution and a 1-km spatial resolution were used to test the proposed method. Compared to traditional lookup table (LUT) algorithms, the results show that AHM not only makes the process of $R_{ns}$ estimation simple and efficient but also has high accuracy in estimating instantaneous all-sky $R_{ns}$. Benefiting from high spatio-temporal resolutions, our daily $R_{ns}$ estimates using GOSE-16 data exhibited superior performance compared to using the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) and 1° Clouds and the Earth’s Radiant Energy System (CERES) product. Using accurate daily $R_{ns}$ estimates and LRD as inputs, the EHM model shows reasonably good results for estimating $R_{n}$ ($R^{2}$ , RMSE, and bias of 0.91, 20.95 W/m2, and −0.05 W/m2, respectively). Maps of 1-km $R_{ns}$ and $R_{n}$ exhibit similar spatial patterns to those from the 1° CERES product, but with substantially more spatial details. Overall, the proposed $R_{n}$ retrieval scheme can accurately estimate all-sky 1-km $R_{ns}$ and $R_{n}$ at mid- to low-latitudes and can be easily adapted and applied to other GOES- 16-like satellites, such as Himawari-8, Meteosat Third Generation (MTG), and Fenyun-4. This study demonstrates the advantages of estimating $R_{n}$ using geostationary satellites with improved accuracy and resolutions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The MEdium-Resolution Spectral Imager (MERSI), onboard the second-generation Chinese polar-orbit meteorological satellite FY-3A, is a MODIS-like sensor with 19 solar bands and one thermal infrared band. Although there is a visible onboard calibration device, it can only be used for tracking temporal instrument degradation. The vicarious calibration (VC) campaign at the Dunhuang site, conducted once a year, has been the main postlaunch absolute radiometric calibration method for MERSI in the solar bands. To increase the in-flight calibration frequency, a multisite radiometric calibration tracking method is presented. This method relies on simulated radiation over several stable sites, and a daily calibration updating model is built from long-term trending of calibration coefficient series. The MERSI calibration reference is evaluated against the observations of Aqua MODIS, showing mean relative biases within 5% from 0.4 to 2.1 μm . The short-wave channels of MERSI are found to experience large degradation, particularly the 412-nm band with an annual degradation rate of 9.7%, whereas the red and near-infrared bands are relatively stable with annual degradation rates within ±1%. Several approaches have been used to analyze the reliability of MERSI calibration results. A comparison of the calibration slopes shows that the relative biases between the multisite method and the annual Dunhuang VC campaign are below 3.8%. Aqua MODIS is used as a reference to monitor the data quality of the recalibrated MERSI. A double-difference analysis shows that the mean relative biases are almost within 5% over stable deserts, and the synchronous nadir observation analysis also reveals good agreement. [ABSTRACT FROM AUTHOR]

This paper presents details of techniques for generating thermal infrared and visible composite images from the cloud-free portions of temporally closely spaced MODerate resolution Imaging Spectroradiometer (MODIS) images, with a focus on studies of landfast sea ice along the East Antarctic coast. Composite image inclusion criteria are based on modified MODIS Earth Observing System cloud mask product results. The compositing process presented places emphasis on retaining maximum spatial resolution while minimizing storage space requirements. Composite images can be produced either as a regular product (e.g., on a ten-day grid), or dynamically (whenever enough information is acquired to produce a new output image). The techniques presented are applicable at any latitude, are available for all MODIS channels at their native resolution, can combine Aqua and Terra images, and can produce maps in any output projection. However, due to the polar orbit of NASA's Terra a Aqua satellites which host the MODIS instrument, more frequent coverage is produced at higher latitudes. Thus, the techniques presented are particularly applicable to polar research. Examples of composite image generation of the landfast sea ice around the Mertz Glacier region, East Antarctica, are included for both winter and summer. [ABSTRACT FROM AUTHOR]

State-of-the-art cloud products are typically generated using scientific polar orbiting satellites such as the Moderate Resolution Imaging Spectroradiometer (MODIS). However, they do not allow for observation of the same region at a regular temporal frequency, rendering them ineffectual for nowcasting problems. Operational satellites such as Meteosat-8 SEVIRI, in contrast, are geostationary and provide continual data at a regular temporal frequency over a much larger region. MODIS-like cloud products cannot be directly generated from operational satellites as they typically have a smaller number of spectral bands and different wavelengths and spatial resolution. This paper applies the canonical coordinate decomposition method to estimate scientific cloud products using imagery from operational satellites. Using the proposed method features of the Meteosat-8 imagery data that are maximally coherent with the data from the MODIS are generated. These features are temporally updated at times and locations where MODIS data are unavailable using the alternating block power method. A subset of the canonical coordinates of Meteosat-8 SEVIRI is then used to create MODIS-like cloud products using several neural networks. The quality of the generated cloud products and their temporal consistency have been demonstrated on several data sets from July 2004. A benchmarking with an independent Meteosat-8-based algorithm is also provided, which shows the promise of our approach in generating MODIS-like cloud products. [ABSTRACT FROM AUTHOR]