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The on-orbit radiometric calibration is a fundamental task in quantitative remote sensing applications. A widely used calibration method is the cross-calibration based on Simultaneous Nadir Observation (SNO), which involves using high-precision reference instruments to calibrate lower-precision onboard instruments. However, despite efforts to match the observation time, spatial location, field geometry, and instrument spectra, errors can still be introduced during the matching processes and linear regression analysis. This paper focuses on the error generated by sample matching and the error fitting method generated by the sample fitting method. An error propagation analysis is performed to develop a generic model for assessing the uncertainty of the SNO cross-calibration method itself in meteorological satellite infrared channels. The model is validated using the payload parameters of the Hyperspectral Infrared Atmospheric Sounder (HIRAS) and the Medium Resolution Spectral Imager (MERSI) instruments aboard the FengYun-3D (FY-3D). Simulation experiments are performed considering typical bright temperatures, different background fields, and varying matching threshold conditions. The results demonstrate the effectiveness of the proposed model in capturing the error propagation chain in the SNO cross-calibration process. The model provides valuable insight into error analysis in the SNO cross-calibration method and can assist in determining the optimal sample matching threshold for achieving radiometric calibration accuracy. [ABSTRACT FROM AUTHOR]

The existence of significant cross-polar antenna patterns, as well as the scan-dependent measurement biases, inherent to the polarimetric phased array radar (PPAR), are among the most important risk factors for using this technology in weather observations. The cross-polar patterns on receive induce cross coupling between returns from the two orthogonal fields causing biases in polarimetric variable estimates. Furthermore, the electromagnetic coupling in hardware may exacerbate the cross-coupling effects. To address this problem, a pulse-to-pulse phase coding in either the horizontal or vertical ports of the transmission elements has been proposed. However, it does not affect the scan-dependent system biases in PPAR estimates, which require corrections via calibration mechanisms. Further, the cross-coupling signals are proportional to the cross-polar pattern power levels, rendering mitigation effective only at steering angles where these levels are sufficiently low (e.g., approximately less than −25 dB). In that regard, any approach that augments the number of such steering angles benefits the cross-coupling mitigation effectiveness. Herein, a simple approach that has a potential to achieve this via antenna tilt is presented. Significance Statement: The issue of biases caused by cross coupling (due to significant cross-polar patterns in PPARs) is one of the biggest challenges for their weather observation applications. Numerous approaches have been proposed to address this issue, but none has been comprehensive. This suggests that the solution to the cross-coupling issue should be a combination of various mitigation approaches. In that regard, it is suggested in this work that a small antenna tilt can aid in the mitigation of the cross-coupling issue. [ABSTRACT FROM AUTHOR]

The cloud thermodynamic phase is a crucial parameter to understand the Earth's radiation budget, the hydrological cycle, and atmospheric thermodynamic processes. Spaceborne active remote sensing such as the synergistic radar-lidar DARDAR product is considered the most reliable method to determine cloud phase; however, it lacks large-scale observations and high repetition rates. These can be provided by passive instruments such as SEVIRI aboard the geostationary Meteosat Second Generation (MSG) satellite, but passive remote sensing of the thermodynamic phase is challenging and confined to cloud top. Thus, it is necessary to understand to what extent passive sensors with the characteristics of SEVIRI are expected to provide a relevant contribution to cloud phase investigation. To reach this goal, we collect five years of DARDAR data to model the cloud top phase (CTP) for MSG/SEVIRI and create a SEVIRI-like CTP through an elaborate aggregation procedure. Thereby, we distinguish between ice (IC), mixed-phase (MP), supercooled (SC), and warm liquid (LQ). Overall, 65% of the resulting SEVIRI pixels are cloudy, consisting of 49% IC, 14% MP, 13% SC, and 24% LQ cloud tops. The spatial resolution has a significant effect on the occurrence of CTP, especially for MP cloud tops, which occur significantly more often at the lower SEVIRI resolution than at the higher DARDAR resolution (9%). We find that SC occurs most frequently at high southern latitudes, while MP is found mainly in both high southern and high northern latitudes. LQ dominates in the subsidence zones over the ocean, while IC occurrence dominates everywhere else. MP and SC show little seasonal variability apart from high latitudes, especially in the south. IC and LQ are affected by the shift of the Intertropical Convergence Zone. The peak of occurrence of SC is at −3 ∘ C, followed by that for MP at −13 ∘ C. Between 0 and −27 ∘ C, the occurrence of SC and MP dominates IC, while below −27 ∘ C, IC is the most frequent CTP. Finally, the occurrence of cloud top height (CTH) peaks lower over the ocean than over land, with MP, SC, and IC being undistinguishable in the tropics but with separated CTH peaks in the rest of the MSG disk. Finally, we test the ability of a state-of-the-art AI-based ice cloud detection algorithm for SEVIRI named CiPS (Cirrus Properties for SEVIRI) to detect cloud ice. We confirm previous evaluations with an ice detection probability of 77.1% and find a false alarm rate of 11.6%, of which 68% are due to misclassified cloud phases. CiPS is not sensitive to ice crystals in MP clouds and therefore not suitable for the detection of MP clouds but only for fully glaciated (i.e., IC) clouds. Our study demonstrates the need for the development of dedicated cloud phase distinction algorithms for all cloud phases (IC, LQ, MP, SC) from geostationary satellites. [ABSTRACT FROM AUTHOR]

In this work, we present the most updated catalog of Io hot spots based on Juno/JIRAM data. We find 242 hot spots, including 23 previously undetected. Over the half of the new hot spots identified, are located at high northern and southern latitudes (>70°). We observe a latitudinal variability and a larger concentration of hot spots in the polar regions, in particular in the North. The comparison between JIRAM and the most recent Io hot spot catalogs listing power output (Veeder et al., 2015, https://doi.org/10.1016/j.icarus.2014.07.028; de Kleer, de Pater, et al., 2019, https://doi.org/10.3847/1538-3881/ab2380), shows JIRAM detected 63% and 88% of the total number of hot spots, respectively. Furthermore, JIRAM observed 16 of the 34 faint hot spots previously identified. JIRAM data revealed thermal emission from 5 dark pateræ inferred to be active from color ratio images, thus confirming that these are hot spots. Plain Language Summary: We mapped the hot spot distribution on Io's surface by analyzing the images acquired by the JIRAM instrument onboard the Juno spacecraft. We identified 242 hot spots, including 23 not present in other catalogs. A large number of the new hot spots identified are in the polar regions, specifically in the northern hemisphere. The comparison between our work and the most recent and updated catalog reveals that JIRAM detected 82% of the most powerful hot spots previously identified and half of the intermediate‐power hot spots, thus showing that these are still active. JIRAM detected 16 out of the 34 faint hot spots previously reported. The resolution of JIRAM may not have been sufficient to detect these faint hot spots, or activity might have faded or stopped. Key Points: We produced a new Io hot spot map based on Juno/JIRAM dataWe identified 242 hot spots, including 23 previously undetectedThe latitudinal hot spot distribution is uneven with a larger concentration at the poles [ABSTRACT FROM AUTHOR]

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 science operations of the spacecraft and remote sensing instruments for the Martian Moon eXploration (MMX) mission are discussed by the mission operation working team. In this paper, we describe the Phobos observations during the first 1.5 years of the spacecraft's stay around Mars, and the Deimos observations before leaving the Martian system. In the Phobos observation, the spacecraft will be placed in low-altitude quasi-satellite orbits on the equatorial plane of Phobos and will make high-resolution topographic and spectroscopic observations of the Phobos surface from five different altitudes orbits. The spacecraft will also attempt to observe polar regions of Phobos from a three-dimensional quasi-satellite orbit moving out of the equatorial plane of Phobos. From these observations, we will constrain the origin of Phobos and Deimos and select places for landing site candidates for sample collection. For the Deimos observations, the spacecraft will be injected into two resonant orbits and will perform many flybys to observe the surface of Deimos over as large an area as possible. [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]

Evapotranspiration (ET) estimates based on remote sensing are constrained by the spatial and temporal resolution of spaceborne observations. Therefore, the choice to be made is whether modelling at high temporal resolution with geostationary satellites at the cost of the spatial detail, or spatially detailed instantaneous modelling at overpass-time with polar-orbit satellites. This study aimed at exploiting the strength of both modelling approaches and derive daily ET estimates with cell size 1.0 km. The study was conducted in four sites with different land cover arrangements and catalogued as wetlands. The methodological approach was based on the algorithm behind the operational ET product of the Satellite Application Facility on Land Surface Analysis (LSA-SAF) initiative. The examined period comprised the end of the Spot-Végétation (Spot-V) mission and the first year of Proba-V operational modus. Two aspects were investigated: 1. The adequacy of combining observations from geostationary and Spot-V and Proba-V satellite missions for obtaining the target product; and, 2. The intercompatibility of ET estimates based on Spot-V and Proba-V so that continuous series over the operational period of both missions could be built. The comparison of simulated ET with in situ measured fluxes (eddy covariance) showed that the introduction of more spatial detail through polar-satellite observations resulted in the reduction of bias and root-mean-square error in ET estimates, as compared with estimations where Earth observation data were taken from geostationary satellites only. Moreover, the signals derived from Spot-V and Proba-V exhibited similarity during the period of operational overlap; especially in non-cultivated surfaces. This suggests that continuity objective of these two missions can be exploited in the estimation of ET time series. [ABSTRACT FROM AUTHOR]

Diurnal land surface temperature cycle (DTC) models are useful tools for generating continuous diurnal land surface temperature (LST) dynamics from temporally sparse satellite observations. Four-parameter DTC models (FPD) can be applied to tandem polar-orbiting satellite observations that sample the surface at least four times per day and, therefore, they have received especial attention. Different approaches have been proposed to reduce the parameter number of DTC models to only four, but a comprehensive and systematic comparison of the published FPDs and their performance is lacking. In addition, it remains unclear whether there are even better parameter-reduction approaches (PRAs) for DTC modeling when only four observations per day are available. Consequently, we chose three semi-empirical DTC models (GOT01, INA08, and GOT09) and one quasi-physical DTC model (GEM) and obtained nine FPDs with PRAs (e.g., by fixing some of the DTC parameters as constants). Using in-situ thermal observations from the U.S. Climate Reference Network, as well as LSTs from the geostationary MSG and FY-2F satellites under clear sky, we compared the performances of these nine FPDs for 24 and 4 available LST observations per day. We obtained the following results: (1) The GOT09- and GEM-type models generally performed better than the other models with in-situ measurements, while the INA08-ts and GOT09-type models possessed high accuracies for the geostationary LSTs. (2) For the semi-empirical models, the PRA ‘ t s  =  t ss  − 1’ (where t s and t ss are the onsets of nighttime cooling and sunset, respectively) is generally more accurate than the PRA ‘ δT  = 0’ (where δT is the day-to-day change of residual temperature). The only exception is the GOT09-type model, for which the ‘ t s  =  t ss  − 1’ strategy is less accurate. (3) GOT09-dT-τ, which fixes δT as zero and the atmospheric optical thickness ( τ ) as 0.01 for parameter reduction, shows the best performance of the FPDs. The study gives an overview of commonly-used four-parameter DTC models, provides a foundation for generating spatio-temporally continuous LST products, and offers guidance for choosing four-parameter DTC models in various applications. [ABSTRACT FROM AUTHOR]

This study demonstrates that Global Hawk unmanned aircraft system dropwindsondes and Atmospheric Infrared Sounder (AIRS) observations can be complementary in sampling a tropical cyclone (TC). The assimilation of both datasets in a regional ensemble data assimilation system shows that the cumulative impact of both datasets is greater than either one alone because of the presence of mutually independent information content. The experiment that assimilates both datasets has smaller position and intensity errors in the mean analysis than those with individual datasets. The improvements in track and intensity forecasts that result from combining both datasets also indicate synergistic benefits. Overall, superior track and intensity forecasts are evident. This study suggests that polar-orbiting satellite spatial coverage should be considered in operational reconnaissance mission planning in order to achieve further improvements in TC analyses and forecasts. [ABSTRACT FROM AUTHOR]

A significant difficulty in urban studies is obtaining urban areas. Nighttime light (NTL) data provide efficient approaches to map urban areas. Previous methods have utilized visual particularities of cities with ancillary data to obtain the optimal thresholds. How cities behave differently from rural areas should be considered. A Zipf's law-based method is proposed to bootstrap the optimal threshold based on the statistical properties of a Zipf's law model on continuous thresholds at the country scale. In our method, the Zipf's law model is utilized to quantify fractal, self-organized, and agglomeration behaviors of cities. The three-phase cluster dynamics are discovered and the abrupt transition between Phase 1 and Phase 2 denotes the rural-urban demarcation point. The urban areas are derived by the proposed method from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data in 2013 in China. An accuracy assessment is conducted to compare it with the GlobeLand30-2010 data and the overall accuracy has directly confirmed the effectiveness of the method. The validation using point of interest (POI) data verifies that the urban areas show strong responses to social interactions and production with R2 values of 0.91 and 0.92, respectively, implying that the city areas extracted by our method can be a proxy for human activities. Comparisons with existing methods validate the effectiveness and high degree of automation of the proposed method in mapping urban areas at the country level. According to our analyses, the Zipf's law-based method shows great potential to provide a universal criterion to map urban areas from the perspective of the behaviors of urban systems without ancillary data, and a valuable tool for spatial and temporal urban studies. [ABSTRACT FROM AUTHOR]

A long-term Northern Hemisphere (NH) daily 5-km snow cover extent (SCE) product (JASMES) was developed by the application of a consistent objective snow cover mapping algorithm to data from historical optical sensors on polar orbiting satellites from 1978 to 2015. A conventional decision tree algorithm with multiple threshold tests was employed to analyze radiances for the five spectral bands available across the full analysis period. The accuracies of the analyzed SCE maps were evaluated against in-situ snow data measured at ground stations along with the SCE maps from the National Oceanic and Atmospheric Administration Climate Data Record (NOAA-CDR) product. The evaluation showed the JASMES product to have a more temporally stable producer's accuracy (PA; 1–omission error) than NOAA, which is a key factor in the analysis of long-term SCE trends. Comparison of seasonal NH SCE trends from the two products showed NOAA to have opposite trends to those of JASMES in the fall and winter seasons, and to have overestimated SCE decreasing trends in the spring and summer. These tendencies are consistent with the increasing spatial and temporal resolutions of information over time, which were used in generating the NOAA snow analysis. An estimation of unbiased SCEs based on the accuracies of SCE maps also endorses the long-term trends of the JASMES product. The JASMES NH seasonal SCE exhibited negative slopes in all seasons but was only statistically significant in the summer (JJA) and fall (SON). Delayed snow cover onset was observed to be the main driver of decreasing annual snow duration (SCD) trends. The spatial pattern of annual SCD trends exhibited noticeable asymmetry between continents, with the largest significant decreases observed over western Eurasia with relatively few statistically significant decreases over North America. [ABSTRACT FROM AUTHOR]

This study assesses the impact assimilating the scatterometer near-surface wind observations and total precipitable water from the SSMI, into WRF on genesis and track forecasting of four tropical cyclones (TCs). These TCs are selected to be representative of different intensity categories and basins. Impact is via a series of data denial experiments that systematically exclude the remote sensed information. Compared with the control case, in which only the final analysis atmospheric variables are used to initialize and provide the lateral boundary conditions, the data assimilation runs performed consistently better, but with very different skill levels for the different TCs. Eliassen-Palm flux analyses are employed. It is confirmed that if a polar orbital satellite footprint passes over the TC’s critical genesis region, the forecast will profit most from assimilating the remotely sensed information. If the critical genesis region lies within an interorbital gap then, regardless of how strong the TC later becomes (e.g., Katrina 2005), the improvement from assimilating near-surface winds and total precipitable water in the model prediction is severely limited. This underpins the need for a synergy of data from different scatterometers/radiometers. Other approaches are suggested to improve the accuracy in the prediction of TC genesis and tracks. [ABSTRACT FROM AUTHOR]

This paper discusses the differences, due to different measuring mechanisms, of satellite-derived and ground measured surface solar irradiance (SSI) direct and diffuse components, and the impacts of the differences when validating satellite-derived data using ground measurements, using SSI products derived from the Advanced Himawari Imager. The issue of satellite-ground mismatch, though previously recognized, has not been given sufficient attention in the relevant scientific literature due to: (i) satellite SSI have mostly focused on relatively longer-term data, such as hourly, daily and longer; and/or (ii) most studies assessed only global (or total) irradiance. Also, studies addressing this satellite-ground mismatch issue often require a dense ground network to characterize the spatial representativeness of ground measurements, which likely limited performing such studies. With shorter-term satellite SSI data, such as 10-min or less, and the direct and diffuse components becoming increasingly available, further investigation of this satellite-ground mismatch issue is warranted. Our analysis, using direct and diffuse component SSI observations from satellite and sparse ground sites showed that a large part of apparent validation errors may have been due to the satellite-ground mismatch, often exceeding 50% in the case of 10-min direct normal irradiance. Greater appreciation and understanding of this issue should lead to more objective assessment of the real quality of satellite SSI products. Our study also showed that several factors, such as spatial averaging, temporal averaging, solar zenith angle and view zenith angle, all have often substantial impacts on the validation outcomes, over 100 W/m2 in some cases, highlighting the necessity for the ground- and satellite-based solar radiation communities to co-develop protocols for satellite SSI data validation. • Discussing ground-satellite mismatch of the direct and diffuse components. • Quantifying impact of the mismatch on validating the components. • Helping objective perception on quality of satellite-derived products. • Raising community awareness and promoting the development of protocols. • This study did not require a dense ground network to quantify the mismatch. [ABSTRACT FROM AUTHOR]

Volcanic activity is observed worldwide with a variety of ground and space-based remote sensing instruments, each with advantages and drawbacks. No single system can give a comprehensive description of eruptive activity, and so, a multi-sensor approach is required. This work integrates infrared and microwave volcanic ash retrievals obtained from the geostationary Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI), the polar-orbiting Aqua-MODIS and ground-based weather radar. The expected outcomes are improvements in satellite volcanic ash cloud retrieval (altitude, mass, aerosol optical depth and effective radius), the generation of new satellite products (ash concentration and particle number density in the thermal infrared) and better characterization of volcanic eruptions (plume altitude, total ash mass erupted and particle number density from thermal infrared to microwave). This approach is the core of the multi-platform volcanic ash cloud estimation procedure being developed within the European FP7-APhoRISM project. The Mt. Etna (Sicily, Italy) volcano lava fountaining event of 23 November 2013 was considered as a test case. The results of the integration show the presence of two volcanic cloud layers at different altitudes. The improvement of the volcanic ash cloud altitude leads to a mean difference between the SEVIRI ash mass estimations, before and after the integration, of about the 30%. Moreover, the percentage of the airborne "fine" ash retrieved from the satellite is estimated to be about 1%-2% of the total ash emitted during the eruption. Finally, all of the estimated parameters (volcanic ash cloud altitude, thickness and total mass) were also validated with ground-based visible camera measurements, HYSPLIT forward trajectories, Infrared Atmospheric Sounding Interferometer (IASI) satellite data and tephra deposits. [ABSTRACT FROM AUTHOR]

The day-night band (DNB) low-light-level visible sensor, mounted on the Suomi-National Polar-Orbiting Partnership ( SNPP) satellite, can measure visible radiances from the earth and atmosphere (solar/lunar reflection, and natural/anthropogenic nighttime light emissions) during both day and night and can achieve unprecedented nighttime low-light-level imaging with its accurate radiometric calibration and fine spatiotemporal resolution. Based on the good characteristics of DNB, a multichannel threshold (MCT) algorithm combining DNB with other Visible-Infrared Imager-Radiometer Suite (VIIRS) channels is proposed to monitor nighttime fog/low stratus. Through a gradual separation of the underlying surface (land, vegetation, water bodies, and city lights), snow, and high/medium clouds, a fog/low-stratus region can ultimately be extracted by the algorithm. Then, the algorithmic feasibility is verified by three typical cases of heavy fog/low stratus in China. The experimental results demonstrate that the outcomes of the MCT algorithm approximately coincide with the ground-measured results. Furthermore, the MCT algorithm shows promise for nighttime fog/low-stratus detection in some example cases with about a 0.84 average probability of detection (POD), a 0.73 average critical success index (CSI), and a 0.15 average false alarm ratio (FAR), which reveals some improvement over the conventional dual-channel difference (DCD) algorithm. [ABSTRACT FROM AUTHOR]

The launch of VIIRS on-board the Suomi-National Polar-orbiting Partnership (S-NPP) on 28 October 2011, marked the beginning of the next chapter on nighttime lights observation started by the Defense Meteorological Satellite Program's (DMSP) OLS sensor more than two decades ago. The VIIRS observes the nighttime lights on Earth through its day-night band (DNB), a panchromatic channel covering the wavelengths from 500 nm to 900 nm. Compared to its predecessors, the VIIRS DNB has a much improved spatial/temporal resolution, radiometric sensitivity and, more importantly, continuous calibration using on-board calibrators (OBCs). In this paper, we describe the current state of the NASA calibration and characterization methodology used in supporting mission data quality assurance and producing consistent mission-wide sensor data records (SDRs) through NASA's Land Product Evaluation and Analysis Tool Element (Land PEATE). The NASA calibration method utilizes the OBCs to determine gains, offset drift and sign-to-noise ratio (SNR) over the entire mission. In gain determination, the time-dependent relative spectral response (RSR) is used to correct the optical throughput change over time. A deep space view acquired during an S-NPP pitch maneuver is used to compute the airglow free dark offset for DNB's high gain stage. The DNB stray light is estimated each month from new-moon dark Earth surface observations to remove the excessive stray light over the day-night terminators. As the VIIRS DNB on-orbit calibration is the first of its kind, the evolution of the calibration methodology is evident when the S-NPP VIIRS's official calibrations are compared with our latest mission-wide reprocessing. In the future, the DNB calibration methodology is likely to continue evolving, and the mission-wide reprocessing is a key to providing consistently calibrated DNB SDRs for the user community. In the meantime, the NASA Land PEATE provides an alternative source to obtain mission-wide DNB SDR products that are calibrated based on the latest NASA DNB calibration methodology. [ABSTRACT FROM AUTHOR]

Abstract: Ocean colour sensors have been capturing the state of the world's oceans for over a decade. They are typically installed on polar-orbiting satellites and cover the entire earth every 1 to 2days. This temporal resolution is insufficient to observe oceanic processes occurring at a higher frequency, especially when taking cloud cover into account. Data from geostationary platforms can be obtained with a much better temporal resolution (images every 15 or 60min), and thus are useful to study those processes. We show that by synergistically combining marine reflectance data from SEVIRI, a geostationary sensor, and MODIS Aqua, a polar orbiter, the resulting product is an improvement over both data sources. The synergy approach takes the reflectance from MODIS, with high quality and high spatial resolution, and modulates this over the day by the temporal variability of the SEVIRI reflectance, normalized to the SEVIRI reflectance at the time of MODIS overpass. The temporal frequency of the synergy product is much better than that of MODIS, and by using the latter's high quality data, the limited spatial and radiometric resolution of SEVIRI is enhanced. As the SEVIRI data is limited to a single broad red band (560–710nm), the applications of the synergy product are limited to parameters that can be derived from this band, such as suspended particulate matter (SPM), turbidity (T) and the diffuse attenuation of photosynthetically available radiation (Kpar) in turbid waters. A geostationary ocean colour sensor over Europe will provide invaluable data concerning our marine environment. The cost of increasing the spatial resolution of a geostationary sensor is very high, and this study illustrates that a lower resolution geostationary ocean colour sensor combined with a high resolution polar orbiting sensor, can provide a high frequency synergetic product with high spatial resolution. [Copyright &y& Elsevier]

Abstract: The Suomi National Polar-orbiting Partnership (SNPP) sensors operationally measure a broad spectrum from microwave to ultraviolet wavelengths for generating 30 satellite products. The wide swath of the SNPP Visible Infrared Imaging Radiometer Suite (VIIRS) observed a historic event: 3 typhoons that all hit China mainland within 5days. The Community Radiative Transfer Model (CRTM) provides critical supports to the SNPP instrumental validation and verification efforts. For example, the CRTM helped to verify image striping in the Advanced Technology Microwave Sounder (ATMS) upper atmosphere channels. The CRTM has also been used to characterize the ATMS radiometric bias and has led to the development of a complementary cloud screening method. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) 6h analysis data as inputs to the CRTM, we can statistically quantify the spectral bias for each field of view (FOV) of the Cross-track Infrared Sounder (CrIS). The CRTM is also a very useful tool for cross-sensor verifications. Using the double difference method, it can remove the biases caused by slight differences in the spectral response and geometric angles between two instruments. The CRTM helps our understanding on radiometric and spectral calibrations. It is the CRTM simulations that enable us to determine the root cause of the VIIRS shortwave infrared band image striping during daytime. The CRTM is operationally used at the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) for weather forecasting and monitoring satellite radiance biases and standard deviation. This study also demonstrated the CRTM capability for Ozone Mapping and Profiler Suite (OMPS) radiance simulations. The first result showed a good agreement between the measurement and simulation. The CRTM for OMPS limb sensing, and Clouds and the Earth's Radiant Energy System (CERES) shortwave radiation and long-wave radiation flux simulation capability need to be extended. [Copyright &y& Elsevier]

The Suomi National Polar-Orbiting Partnership ( S-NPP) satellite was successfully launched on 28 October 2011. It carries five new-generation instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS). The VIIRS is a whiskbroom radiometer that scans the surface of the earth using a rotating telescope assembly, a double-sided half-angle mirror, and 16 individual detectors. Substantial efforts are being made to accurately calibrate all detectors in orbit. As of this writing, VIIRS striping is reduced to levels below those seen in corresponding Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) bands and meets the program specifications and requirements. However, the level 2 SST products derived from level 1 sensor data records (SDRs) thermal emissive bands still show residual striping. These artifacts reduce the accuracy of SST measurements and adversely affect cloud masking and the output of downstream applications, such as thermal front detection. To improve the quality of SST imagery derived from the VIIRS sensor, an adaptive algorithm was developed for operational use within the National Environmental Satellite, Data, and Information Service (NESDIS)'s SST system. The methodology uses a unidirectional quadratic variational model to extract stripe noise from the observed image prior to nonlocal filtering. Evaluation of the algorithm performance over an extended dataset demonstrates a significant improvement in the Advanced Clear-Sky Processor for Oceans (ACSPO) VIIRS SST image quality, with normalized improvement factors (NIF) varying between 5% and 25%. [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]

The polar-orbiting satellite sensor, which can observe the entire Earth's surface with good spatial and spectral resolution, is a potential tool for estimation of global downward shortwave radiation (DSR). However, it can only provide a couple of snapshots in one day, which should be extended to daily-integrated DSR in order to drive the ecosystem models. In this paper, the temporal and spatial uncertainties for estimating of daily-integrated DSR from instantaneous measurements of polar-orbiting satellites are evaluated using geostationary Geosynchronous Meteorological Satellite-5 ( GMS-5) observations over East Asia. Those overpass times-including 1030, 1200, and 1330 local time (LT), which correspond to Terra/Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and Aqua/MODIS-are evaluated. The combinations of multiobservations are also assessed. The results show that the daily-integrated DSR from polar-orbiting satellite observations of 1030, 1200, and 1330 underestimate solar radiation by 2.16% (0.46 MJ m−2), 5.44% (1.16 MJ m−2), and 5.54% (1.09 MJ m−2), with an RMSE of 2.05 MJ m−2 (12.92%), 2.50 MJ m−2 (13.33%), and 2.34 MJ m−2 (13.95%) in East Asia with large spatial and seasonal variations. In general, the bias is higher in the southern than in the northern part of East Asia. It is low in January, February, and March, then increases from April and reaches the maximum in June and July, and decreases rapidly to lower than 1.0 MJ m−2 in October. The uncertainties of daily-integrated DSR could be reduced by averaging multiday observations or combining of multitime observations. These uncertainties' distributions are important for evaluation the usability of daily-integrated DSR from polar-orbiting satellite data and probably can be used for its calibration. [ABSTRACT FROM AUTHOR]

Abstract: In this study, ICESat altimetry data are used to provide precise lake elevations of the Tibetan Plateau (TP) during the period of 2003–2009. Among the 261 lakes examined ICESat data are available on 111 lakes: 74 lakes with ICESat footprints for 4–7years and 37 lakes with footprints for 1–3years. This is the first time that precise lake elevation data are provided for the 111 lakes. Those ICESat elevation data can be used as baselines for future changes in lake levels as well as for changes during the 2003–2009 period. It is found that in the 74 lakes (56 salt lakes) examined, 62 (i.e. 84%) of all lakes and 50 (i.e. 89%) of the salt lakes show tendency of lake level increase. The mean lake water level increase rate is 0.23m/year for the 56 salt lakes and 0.27m/year for the 50 salt lakes of water level increase. The largest lake level increase rate (0.80m/year) found in this study is the lake Cedo Caka. The 74 lakes are grouped into four subareas based on geographical locations and change tendencies in lake levels. Three of the four subareas show increased lake levels. The mean lake level change rates for subareas I, II, III, IV, and the entire TP are 0.12, 0.26, 0.19, −0.11, and 0.2m/year, respectively. These recent increases in lake level, particularly for a high percentage of salt lakes, supports accelerated glacier melting due to global warming as the most likely cause. [Copyright &y& Elsevier]

Comparisons are described for infrared-derived cloud products retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) using measured spatial response functions obtained from prelaunch AIRS calibration. One full day (1 January 2005) of global collection-5 MODIS and version-5 AIRS retrievals of cloud-top temperature Tc, effective cloud fraction f, and derived effective brightness temperature Tb, e is investigated. Comparisons of Tb, e demonstrate that MODIS and AIRS are essentially radiatively consistent and that MODIS Tb, e is 0.62 K higher than AIRS Tb, e for all scenes, increasing to 1.43 K for cloud described by AIRS as single layer and decreasing to 0.50 K for two-layer clouds. Somewhat larger differences in Tc and f are observed between the two instruments. The magnitudes of differences depend partly on whether MODIS uses a CO2-slicing or 11- μμm brightness temperature window retrieval method. Some cloud- and regime-type differences and similarities between AIRS and MODIS cloud products are traceable to the assumptions made about the number of cloud layers in AIRS and also to the MODIS retrieval method. This (partially) holistic comparison approach should be useful for ongoing algorithm refinements, rigorous assessments of climate applicability, and establishment of the capability of synergistic MODIS and AIRS retrievals for improved cloud quantities and also should be useful for future observations to be made by the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP). [ABSTRACT FROM AUTHOR]

Conventional methods of viewing and combining retrieved geophysical fields from polar-orbiting satellites often complicate the work of end users because of the erratic time differences between overpasses, the significant time gaps between elements of a composite image, or simply the different requirements for interpretation between contributing instruments. However, it is possible to mitigate these issues for any number of retrieved quantities in which the tracer lifetime exceeds the sampling time. This paper presents a method that uses ''advective blending'' to create high-fidelity composites of data from polar-orbiting satellites at high temporal resolution, including a characterization of error as a function of time gap between satellite overpasses. The method is especially effective for tracers with lifetimes of longer than 7 h. Examples are presented using microwave-based retrievals of total precipitable water (TPW) over the ocean, from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) Morphed Integrated Microwave Imagery at CIMSS TPW product (MIMIC-TPW). The mean average error of a global 0.25°° ×× 0.25°° product at 1-h resolution is 0.5--2 mm, which is very reasonable for most applications. [ABSTRACT FROM AUTHOR]

Abstract: The subject of this study is to investigate the capability of spaceborne remote sensing data to predict ground concentrations of PM10 over the European Alpine region using satellite derived Aerosol Optical Depth (AOD) from the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI) and the polar-orbiting MODerate resolution Imaging Spectroradiometer (MODIS). The spatial and temporal resolutions of these aerosol products (10km and 2 measurements per day for MODIS, ∼25km and observation intervals of 15min for SEVIRI) permit an evaluation of PM estimation from space at different spatial and temporal scales. Different empirical linear relationships between coincident AOD and PM10 observations are evaluated at 13 ground-based PM measurement sites, with the assumption that aerosols are vertically homogeneously distributed below the planetary Boundary Layer Height (BLH). The BLH and Relative Humidity (RH) variability are assessed, as well as their impact on the parameterization. The BLH has a strong influence on the correlation of daily and hourly time series, whilst RH effects are less clear and smaller in magnitude. Despite its lower spatial resolution and AOD accuracy, SEVIRI shows higher correlations than MODIS (r SEV ∼0.7, r MOD ∼0.6) with regard to daily averaged PM10. Advantages from MODIS arise only at hourly time scales in mountainous locations but lower correlations were found for both sensors at this time scale (r∼0.45). Moreover, the fraction of days in 2008 with at least one satellite observation was 27% for SEVIRI and 17% for MODIS. These results suggest that the frequency of observations plays an important role in PM monitoring, while higher spatial resolution does not generally improve the PM estimation. Ground-based Sun Photometer (SP) measurements are used to validate the satellite-based AOD in the study region and to discuss the impact of aerosols'' micro-physical properties in the empirical models. A lower error limit of 30 to 60% in the PM10 assessment from space is estimated in the study area as a result of AOD uncertainties, variability of aerosols properties and the heterogeneity of ground measurement sites. It is concluded that SEVIRI has a similar capacity to map PM as sensors on board polar-orbiting platforms, with the advantage of a higher number of observations. However, the accuracy represents a serious limitation to the applicability of satellites for ground PM mapping, especially in mountainous areas. [Copyright &y& Elsevier]

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]

Polar orbiting meteorological satellites have been used successfully in many fields in which applications, such as dynamic monitoring, need multi-temporal remote sensing data imaged in the same region but at different times and with different sensors. The basis of these quantitative applications is automatic and synthetic preprocessing of different satellite data. In order to ensure the direct comparability of multi-source satellite National Oceanic & Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) series and FY-1D 1A.5 data, based on its imaging mechanisms, the paper discusses radiant and geometric preprocessing methods used for visual and near-infrared and thermal infrared channels. This is performed by analysing the technical parameters of the satellites and the sensors, theoretical numerical simulation and image data comparison. For synthetic radiance preprocessing, radiance calibration, spectral calibration for different sensors and calibration of the Sun elevation angles or the satellite zenith angles at the imaging time are included. Based on normalization of spatial resolution in scan-line directions, rough geometric correction and precise registration are sequentially processed for geometric preprocessing with a higher precision. Combining the detailed steps introduced in the paper, automatic preprocessing of polar orbiting meteorological satellites can be realized. [ABSTRACT FROM AUTHOR]

The forward radiative transfer routine has been used to remove the artefact caused by modulation on meteorological satellite-sensor responses. This paper describes a direct method where images observed by the infrared channels aboard the Multi-functional Transport Satellite (MTSAT-1R) and Fengyun-2C (FY-2C) geostationary satellites are compared with those by the Advanced Very High Resolution Radiometer (AVHRR) sensor aboard the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites. The calibration differences between the polar orbiting and the geostationary satellites are found to be within 1 K of each other. [ABSTRACT FROM AUTHOR]

Abstract: The role of coastal seas as either a sink or a source of CO2 is subject to a great deal of uncertainty. This uncertainty largely arises from a lack of observations in the coastal zones. Remote sensing offers an avenue for expanding these observations by allowing for the extrapolation of relatively limited data sets of dissolved CO2 (pCO2sw). In this paper, predictive algorithms for pCO2sw that could be applied to remote sensing products were created from a field data set collected from September–October, 2005 in Hudson Bay, Canada. The field data showed that an effective pCO2sw interpolation algorithm could be created using sea surface temperature (SST) as a predictor, and that a slight improvement of the algorithm could be achieved if measurements of absorption due to coloured dissolved organic material (a CDOM) were included. Unfortunately, satellite retrievals of a CDOM did not match well with in situ observations, and so only SST (obtained from the MODIS Aqua sensor) was used to create monthly maps of pCO2sw for the period of August–October. To estimate fluxes of CO2, constructed surfaces of pCO2sw were combined with estimates of gas transfer velocity derived from QuikSCAT wind retrievals, and pCO2air based on field observations. The results of these calculations revealed that Hudson Bay acts as a source of CO2 during August and September, but reverts to a sink of CO2 in October as the water temperature decreases. Overall, a positive flux of 1.60 TgC was estimated for the region during the ice-free season. This result is in contrast to most Arctic or sub-Arctic continental shelf seas, where usually strong absorptions of CO2 are observed. [Copyright &y& Elsevier]

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]

A new over–water dust detection algorithm is developed and applied to the 5–channel Advanced Very High Resolution Radiometer (AVHRR) imager onboard the National Oceanic and Atmospheric Administration series of polar orbiting satellites. The algorithm has been developed to improve the distinction between dust and meteorological clouds for very optically thick dust storms that would have previously been flagged as cloud under the Clouds from AVHRR Extended cloud mask algorithm. The algorithm has been assessed by making daily comparisons with data from the Aerosol Robotic Network and by making a climatological comparison with METEOSAT and Total Ozone Mapping Spectrometer data over a portion of the North Atlantic. The new algorithm improves the separation of clouds and airborne dust. Application of the new product to the 5–channel AVHRR historic data sets can provide information on the global dust signal, especially on an interannual time scale. [ABSTRACT FROM AUTHOR]

Mount Erebus (Antarctica) is a remote and inhospitable volcano, where field campaigns are possible only during the austral summer. In addition to continuously monitoring seismic instruments and video cameras, data from scanners flown aboard polar orbiting space-craft, such as the Thematic Mapper (TM) and Advanced Very High Resolution Radiometer (AVHRR), can contribute to continuous, year-round monitoring of this volcano. Together these data allow measurement of the temperature of, thermal and gas flux from, and mass flux to a persistently active lava lake at Erebus' summit. The monitoring potential of such polar-orbiting instruments is enhanced by the poleward convergence of sub-spacecraft ground-tracks at the Erebus latitudes, permitting more frequent return periods than at the equator. Ground-based observations show that the Erebus lava lake was active with an area of ~2800m2 and sulphur dioxide (SO2) flux of (230 +/- 90)td-1 prior to September 1984. AVHRR-based lake area and SO2 flux estimates are in good agreement with these measurements, giving (2320 +/- 1200)m2 and (190 +/- 100)td-1, respectively, during 1980. However during late-1984 the lava lake became buried, with TM data showing re-establishment of the lake, with a TM-derived surface temperature of 578-903 C, by January 1985. Following these events, ground-based lake area and SO2 flux measurements show that the lake area and SO2 flux was lower (180-630m2 and 9-91td-1, respectively). This is matched by a decline in the AVHRR- and TM-derived rate of magma supply to the lake from 330 167kgs-1 prior to 1984 to 30-76kgs-1 thereafter. Clearly, a reduction in magma supply to, and activity at, the lava lake occurred during 1984. We look forward to using data from such future polar-orbiting sensors as the Moderate Resolution Imaging Spectrometer (MODIS), Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER), Enhanced Thematic Mapper (ETM+) and Advanced Along Track Scanning Radiometer (AASTR) to contribute to high (once a day) temporal resolution measurement and monitoring of activity at this volcano. Such analyses will in turn contribute to a more complete understanding of how this volcano works. [ABSTRACT FROM AUTHOR]

From 2011 to 2015, 49 lava fountains occurred at Etna volcano. In this work, the measurements carried out from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument, on board the Meteosat Second Generation (MSG) geostationary satellite, are processed to realize a proximal monitoring of the eruptive activity for each event. The SEVIRI measurements are managed to provide the time series of start and duration of eruption and fountains, Time Averaged Discharge Rate (TADR) and Volcanic Plume Top Height (VPTH). Due to its temperature responsivity, the eruptions start and duration, fountains start and duration and TADR are realized by exploiting the SEVIRI 3.9 μm channel, while the VPTH is carried out by applying a simplified procedure based on the SEVIRI 10.8 μm brightness temperature computation. For each event, the start, duration and TADR have been compared with ground-based observations. The VPTH time series is compared with the results obtained from a procedures-based on the volcanic cloud center of mass tracking in combination with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) back-trajectories. The results indicate that SEVIRI is generally able to detect the start of the lava emission few hours before the ground measurements. A good agreement is found for both the start and the duration of the fountains and the VPTH with mean differences of about 1 h, 50 min and 1 km respectively. [ABSTRACT FROM AUTHOR]