Benefiting from the ability to penetrate clouds, microwave (MW) can theoretically achieve all-sky monitoring, thus making significant contributions to weather forecasts and climate models. Various MW sea surface emissivity (SSE) models have been applied to the products of spaceborne MW radiometers and assimilation models. However, these models do not consider the impact of rainfall on the MW SSE. In this study, rainfall-induced SSE change was inferred based on the brightness temperature (BT) observed by AMSR2 and in-situ ship measurement OceanRAIN. The neural network-based Fast Atmospheric Parameter Simulators (NN-FAPSs) were constructed for quickly calculating the atmospheric upwelling/downwelling BT and transmissivity and the errors of NN-FAPSs fitting on the SSE were below 0.001±0.001 and 0.01±0.03 at 6.925 GHz and 36.5 GHz, respectively. The SSE estimates show that rainfall causes an increase of SSE (e.g., up to 0.2 at 10.65 GHz), and this phenomenon is more pronounced at low wind speeds and high SST, corresponding to the effects of rainfall-induced local wind and SST on the dielectric constant. The error analysis shows that at 6.925, 7.3, and 10.65 GHz, the accuracy of the SSE is less affected by the accuracy of the input parameters as well as the fitting errors of the NN-FAPSs (Δε were below 0.01±0.01). Additionally, polynomial models for 6.925, 7.3, and 10.65 GHz are proposed to provide the first guess of SSE under rainfall conditions for improving the accuracy of cloud and rainfall products. It is believed that this work helps improve the understanding of rainfall-induced MW SSE changes.

An accurate sea foam emissivity model at microwave frequencies is indispensable for applications of microwave data in satellite data assimilation and remote sensing of atmospheric and surface parameters. This study develops an analytic model for estimating sea foam emissivity by integrating the two-stream radiative transfer approximation with a macroscopic parameterization approach. Sea foam is modeled as a vertically inhomogeneous medium, with an exponential vertical profile based on void fraction and foam thickness. The emissivity for vertical and horizontal polarization is derived by combining the interface reflections with the two-stream radiative transfer solutions. The model is validated against the experimental data and shows a high consistency with incoherent methods, thus demonstrating its robustness for remote sensing and oceanographic applications. A sensitivity analysis is also performed for the key model parameters. It is found that within a frequency ranging from 1 to 37 GHz, the L-band is the most sensitive to sea foam thickness and the sensitivity decreases as the frequency increases. The emissivity is saturated for a specific foam thickness. Furthermore, the algorithm developed in this study includes two additional adjustable parameters, namely, the single-scattering reflectance and the asymmetry factor, which enable the simulation to account for the scattering effects of foam, thereby outperforming the traditional incoherent method.

The Advanced Radiative transfer Modeling System (ARMS) is a fast radiative transfer model for satellite radiance calculations and is also used for the direct assimilation of radiances for numerical weather prediction models (NWP). ARMS can support most visible, infrared, and microwave instruments onboard both polar-orbiting and geostationary orbiting satellites from the US, Europe, and China. Both ARMS and RTTOV are employed as satellite observation operators in the China Meteorological Administration Global Forecasting System (CMA-GFS). To evaluate the performance between ARMS and RTTOV in CMA-GFS V4.0, three groups of data assimilation experiments from August 15, 2021, to August 31, 2022, were conducted. It’s shown that good agreement exists in the simulation bias of satellite radiance from both ARMS and RTTOV. Moreover, from the performance of the analysis field and forecast field, the use of ARMS as the satellite observation operator in the CMA-GFS model achieved better results than RTTOV.

Currently, two microwave radiation imagers (MWRI) are flying onboard Fengyun-3 series (midmorning and afternoon orbits) satellites and provide 10 channels of microwave brightness temperatures. This study will develop a global weekly updated land emissivity atlas for NWP communities for data assimilation applications. Two MWRI data are first intercalibrated for their data consistency. The retrieval algorithms are based on the earlier methodologies (1dvar and analytic). The retrieved atlas are compared with the existing data base (TELSEM2) which is widely used in NWP community for understanding the global consistency, and the results show higher correlation correlations at 10 channels. Moreover, three different emissivity datasets from AMSR-E, TELSEM2 and FY-3D are selected to compare the spatial correlation and mean absolute deviation with 1dvar results (FY-3D). It shows the FY-3D have higher spatial correlation (0.924) with AMSR-E1 and TELSEM2 datasets.

In this paper, a fast polarized bidirectional reflectance distribution function (BRDF) model is developed using the Neural Network (NN). The theoretical polarized BRDF matrix developed by He and Weng is used to generate the training datasets and then they are fed to NN to produce the fast BRDF model.

An updated vector discrete ordinate radiative transfer (VDISORT) model is developed for the advanced radiative transfer modeling system (ARMS). By updating the theory of solving process, the widely-used azimuthal symmetric assumption is avoided so that the updated VDISORT can fully couple the general atmospheric scattering and surface reflection. The model shows good accuracies with several benchmarks. Tests show that the azimuthally symmetric assumption can bring obvious errors into the simulated radiance such as its magnitudes of peaks and its signal distributions. It indicates the anisotropic properties should get attention during the satellite simulations and applications.

It is important to carry out monitoring and assessment of flood disasters to improve the efficiency of disaster prevention and mitigation. This study utilizes China's second-generation geostationary meteorological satellite Feng Yun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) data for the efficient flood monitoring in China through uses of high temporal resolutions. Through the synthesis of multi-temporal images of the flood water body, the influence of clouds is effectively eliminated. According to the band characteristics of AGRI, the Combined Index of NDVI and NIR for Water Body Identification (CIWI) algorithm was used to complete the extraction of flood inundation range on the cloud-cleared images. Comparing with the water body identification information from the high-resolution data of Sentinel 1 satellite, the R2reaches 0.9973, indicating that the extraction results of FY4A are reliable.

FengYun-3G satellite was launched in April 2023, carrying onboard a precipitation measurement radar (PMR). In this study, FY-3G PMR data at Ku and Ka bands are assessed for retrieving cloud hydrometeor contents. A novel algorithm is developed to simultaneously retrieve the liquid and ice water contents (IWCs) and solid particle shapes at varying heights. For Typhoon Khanun, the hydrometeor content retrieved using this algorithm is close to the ERA5 rainwater and snow water at its developing and mature stages. The results show that the PMR minimum detectable reflectivity factor at Ku and Ka bands exceeds the design specifications, with values of 10 and 6 dBz, respectively. Furthermore, PMR signals primarily originate from the backscatter of larger particles, including rain, hail, and graupel. Using a prior relationship between the reflectivity and hydrometeor content at different temperatures, IWC and liquid water content (LWC) are retrieved and validated. It is found that Typhoon Khanun during the mature stage, compared to the development stage, IWC decreases while LWC increases. Moreover, based on Ku and Ka retrieval result, the most frequent shapes of typhoon solid particles are large column aggregate, Evans snow aggregate, gem graupel, and six-bullet rosette. Graupel particles are mainly concentrated in areas with high values of hydrometeor content. In some areas with high values of hydrometeors, large column aggregate is more prevalent, while Evans snow aggregate is mainly distributed in the areas with low values of hydrometeors.

Cloud liquid water path (LWP) quantifies liquid water amount within the atmosphere and is closely related to water cycle, weather, and climate. Passive microwave (MW) observations are powerful tools for retrieving LWP. An empirical relationship between the LWPs and MW brightness temperatures (BTs) can be obtained for conventional retrievals, which consider only the influence of LWP on BTs. However, besides LWP, the cloud vertical extent [e.g., cloud top height (CTH)] can affect MW emission, absorption, and corresponding channel BTs, but it is ignored in conventional retrievals. This study investigates the influences of CTH on MW LWP retrievals, and a CTH-dependent algorithm is developed using CTHs from infrared retrievals. Synthetic radiative transfer simulations are performed to quantify CTH effects on MW channel BTs and to establish the CTH-dependent retrieval coefficients. We use the Advanced MW Scanning Radiometer 2 (AMSR2) observations. Cloud products from Moderate Resolution Imaging Spectroradiometer (MODIS) are collocated to provide the necessary CTH information. Thus, we develop an LWP retrieval algorithm by combining AMSR2 BTs with MODIS CTHs. The results indicate that incorporating CTH information into LWP retrievals enhances the consistency between MW and visible/infrared retrievals. Specifically, the CTH-dependent algorithm showed an improvement in the intraclass correlation coefficient (ICC) and a reduction in mean relative differences (MRDs) by approximately 4% (from 18% to 14%) compared to AMSR2 operational retrievals. The CTH-dependent results are slightly more consistent with the MODIS results than the CTH-independent ones, though it remains important to note that the CTH-dependent retrievals introduce less differences compared to their CTH-independent retrievals.

With the recent availability of realistic azimuthally randomly oriented (ARO) raindrops, it is essential to evaluate the difference of radiative transfer simulations with scattering between the ARO raindrops (i.e., Chebyshev rain drop and liquid spheroid) and the commonly used spherical raindrop. Here, the scattering module of radiative transfer for TOVS (RTTOV-SCATT) was evaluated by a reference atmospheric radiative transfer simulator (ARTS) for the frequencies between 10.65 and 36.5 GHz. The two vector scattering solvers used by ARTS were discrete ordinate iterative (DOIT) and RT4. Both scattering solver difference (SSD) and scattering property difference (SPD) are assessed. It is shown that the brightness temperature (BT) difference between RTTOV-SCATT and DOIT can exceed 2.5 K, statistically higher than the value obtained from previous studies. There exists a size parameter threshold that influences the trend of SSD, and this threshold increases slightly with frequency. RT4 tends to produce lower BTs than DOIT, and the biases range from −0.1 to −0.8 K for different frequencies and surface types. In addition, the permittivity model difference dominates the SPD (biases and STDs range from 0.1 to 0.6 K and 0.4–1.3 K, respectively). The nonspherical shape and orientation yield higher horizontal polarization BT and lower vertical polarization BT. The additional polarization difference introduced by ARO raindrops is 0–3.2 K as a function of total column integrated rain water content. Thus, it is believed that this study offers the error sources and will help the improvements of RTTOV-SCATT.

A cloud-dependent 1-D variational (1DVAR) precipitation retrieval algorithm applied to FengYun-3D microwave soundings (CD1DVAR-FY3DMS) is developed in this study. Compared with the current 1DVAR precipitation retrieval framework, cloud scene identification (CSI) is first proposed to delineate the different weather conditions including clear sky, stratiform clouds, and convective clouds. Results of this study in a typical tropical cyclone event demonstrate that: 1) the precipitation retrievals considering CSI (correlation coefficient ~0.72 and root mean square error ~1.63 mm/h) outperform those without distinguishing cloud scenes (correlation coefficient ~0.66 and root mean square error ~1.82 mm/h); 2) identifying cloud scenes in a variational scheme could significantly improve the accuracy of retrieving heavy precipitation volumes, with the capturing abilities improved by ~100%; and 3) the FengYun-3 constellation has great potential to complement global microwave retrievals. In addition, these findings could provide valuable references and pathfinders for further improving the retrieval accuracy of microwave-based precipitation estimates, especially for strong convective zones.

In this study, a thermal infrared (TIR) based convolutional neural network (TIR-CNN), originally designed for retrieving cloud optical properties from TIR radiometry, is further developed to obtain global cloud base height (CBH) and cloud thermodynamic phase during both daytime and nighttime. It employs TIR radiances, cloud optical properties retrieved by TIR-CNN, altitude, landcover, and lifting condensation level as inputs to estimate global CBH and cloud phase for both single- and multi-layer clouds. This new model is trained, validated, and tested using radar-lidar products from CloudSat/CALIPSO. It provides global CBH with root-mean-square errors of 1.19 km and 1.91 km for single- and multi-layer clouds, respectively. A cloud layer classifier is trained to provide information on the quality of retrieved CBH, with total accuracies of 82% and 85% for single- and multi-layer clouds, respectively. In addition, the new model has remarkable accuracy in identifying the cloud phase within each pixel’s vertical column, particularly in differentiating mixed-phase clouds with an ice cloud top.

We model the sea foam emissivity at frequencies from 1 to 89 GHz. This model is part of the work done by an international science team to develop a radiative transfer model of reference quality for the ocean surface emissivity from L band to infrared frequencies. A study of the sensitivity to different foam properties (foam layer thickness and upper limit of the foam void fraction) guided the effort to tune the foam emissivity model by frequency and polarization. The results show that the differences between simulated and observed brightness temperatures decrease when using the tuned foam model.

Radio frequency interference (RFI) is a key interference source affecting image quality of spaceborne microwave imager. In this study, a channel simulation method based on machine learning is adopted to identify RFI in the low-frequency channel in spaceborne microwave imager. By defining the difference between the observed and the simulated brightness temperatures as the RFI index, the range and intensity affected by RFI in the low-frequency channel can be clearly identified.

Sea salt aerosols are mostly distributed over the oceans and they can significantly affect the atmospheric radiative transfer. This article investigated sea salt aerosol impact on the polarization state of radiance at the top of the atmosphere (TOA) through the use of sea salt particle models. Specifically, six models of sea salt aerosols, including a homogeneous sphere, two super-spheroids, and three inhomogeneous spheres with both spherical and nonspherical cores, were considered and their optical properties were computed using the Lorenz–Mie theory and the invariant imbedding T-matrix method. The polarized radiance at the TOA was simulated by using a vector adding–doubling radiative transfer model. It was demonstrated that the inhomogeneous sphere-modeled TOA polarized radiance had a minimum at the backscattering angles ranging from 170° to 175°, whereas such features disappear when a homogeneous sphere or nonspherical model is used. To prove this effect, the satellite marine aerosol vertical feature mask data from the Cloud Aerosol Light Detection and Ranging (Lidar) and Infrared Pathfinder Satellite Observations (CALIPSO) over global ocean areas were collocated with the measurements from Polarization and Anisotropy of Reflectance for Atmospheric Science Coupled with Observations from a Lidar (PARASOL). It was found that the PARASOL polarized radiance also had negative values at the backscattering angles ranging from 170° to 175°. Thus, the obvious negative polarized radiances at these backscattering angles could be indicative of inhomogeneous sea salt aerosols. Both homogeneous and inhomogeneous sea salt models related to ambient relative humidity (RH) should be considered for accurate radiative transfer simulations.

Aerosol optical depth from different satellite sensors are widely used to estimate surface PM2.5concentrations. However, these products generally have coarse resolutions, limiting the ability to evaluate PM2.5concentrations in urban regions where the human activities are relatively high. This study first develops an ensemble machine learning approach to produce PM2.5concentrations with an extremely high spatial resolution of 250 m, based on Moderate Resolution Imaging Spectroradiometer (MODIS) measurements of top-of-atmosphere reflectance and related meteorological variables. The Yangtze River Delta region, with one of the highest levels of PM2.5pollution in China, is the study region chosen. The model shows a very high and stable performance with a coefficient of determination ($R^{2}$) of 0.90, a root-mean-square error (RMSE) of$12.0~\mu \text{g}/\text{m}^{3}$, a mean prediction error (MPE) of$7.8~\mu \text{g}/\text{m}^{3}$, and a mean relative prediction error (RPE) 16.9% for sample-based cross validation. The model can accurately capture the distribution patterns and magnitudes of PM2.5concentrations over the study region for seasonal mean, daily variations, and different levels of air pollution. The very high resolution of the model has the advantage of capturing the uneven spatial distribution of PM2.5concentrations at small spatial scales and identifying small areas with very high PM2.5concentrations, offering a possible approach for locating the sources of PM2.5emissions. In general, the model developed here estimates very well PM2.5concentrations at a very high spatial resolution, providing detailed information, useful for air-pollution-related studies, as well as pollution monitoring and evaluation by governments, especially in urban and urban-center areas.

Several deliberate treatments are performed to assimilate the microwave sounding observation onboard Chinese polar meteorological satellite FY3D in numerical weather prediction (NWP). The two missing channels of frequency at 23.8 and 31.4 GHz are reconstructed to help the implement of quality control (QC) including the estimation of satellite based cloud liquid water (CLW) and total perceptible water (TPW). Special attention is paid to the significant scan-dependent bias in FY3D microwave temperature observation in bias correction. In addition, the combined microwave sounder (CMWS) data is adopted to use the temperature and humidity observations coherently in ATMS alike one data stream. It is shown that the numerical forecast of typhoon is improved by the assimilation of FY3D CMWS observation.

The MEdium Resolution Spectral Imager-II (MERSI-II) onboard FY-3D satellite is used to retrieve surface vegetation parameters. MERSI TOA reflectances are corrected to surface reflectance and then used to compute normalized differential vegetation index (NDVI) and enhanced vegetation index (EVI) at the canopy levels. MERSI-II VI products are also compared with MODIS data and show a good consistency.

The accurate information on intensity and location of tropical cyclones (TCs) is essential for weather forecasting and warning. With atmosphere thermal profiles retrieved from microwave sounding instruments, the intensity and location of two major hurricanes are estimated based on the atmospheric hydro-static balance equation. Results show that the use of ATMS products derived from the scene-dependent one-dimensional variation (SD1DVAR) could reduce the errors in estimating the location and intensity of tropical cyclones by 52.79% and 36.91%, respectively, compared with the use of NOAA's operational products. Also, using a combined sounding data from FY-3D MWTS with MWHS that has 118 GHz channels could reduce the intensity estimation error by 37.2%. Moreover, SD1DVAR performs very well for both ATMS and MWTS/MWHS. More instruments will be tested to generate a global TC dataset with high temporal resolution.

Satellite-derived aerosol optical depth (AOD) has been widely used to estimate surface fine particulate matter (PM2.5) concentrations. To understand the temporal and spatial variation of PM2.5distribution at various scales, we developed techniques for estimating hourly PM2.5concentration from visible and infrared imagers onboard Himawari-8, GK-2A and FY-4A satellites. During daytime, satellite observed reflectance at the top of atmosphere (TOA) is directly converted to PM2.5through a machine-learning algorithm. At night, the thermal channel data from satellites are tested for PM estimation through using the aerosol absorbing properties. It is found that the PM concentration is higher in the morning and lower in the afternoon in east central China. PM concentrations also exhibit a north-to-south decreasing gradient with the highest one in winter and the lowest in summer. From AHI PM data collected in the past five years, we found a decreasing trend of PM2.5concentration in most of areas of China.

The uncertainty of scattering characteristics of ice particles is an important source of errors in radiative transfer simulation and remote sensing inversion. In this study, the scattering properties of non-spherical ice particles have been analyzed in the microwave range using discrete dipole approximation (DDA) method. For a high simulation accuracy, the number of dipoles for a single particle in DDA should be larger than 50,000, and the orientation average should be no less than 8 * 9 * 8 angles. It is shown that the shape, aspect ratio, and temperature of non-spherical ice particles have significant effects on the microwave scattering properties of particles. The simulation results from this study can be used for formulating the fast lookup tables in radiative transfer calculation in microwave frequencies.

The microwave humidity sounder (MWHS) onboard the Fengyun-3D satellite is providing the data for profiling atmospheric temperature and moisture and has become an important data source for improving the weather forecasts. In this article, three data sources are utilized for assessing the MWHS on-orbit performance, including Global Navigation Satellite System Occultation Sounder (GNOS), ECMWF (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA)-Interim reanalysis, and Advanced Technology Microwave Sounder (ATMS) data. GNOS-retrieved atmospheric profiles and the reanalysis data are used as inputs to the community radiative transfer model (CRTM) for simulating the MWHS brightness temperatures at the top of the atmosphere in July 2018 for characterizing the instrument performance. Since ATMS is a well-calibrated microwave sounding instrument onboard both Suomi NPP and NOAA-20 satellites, its measurements are also collocated with MWHS data for a consensus analysis using the simultaneous nadir overpasses (SNOs) method. In comparing GNOS simulations, MWHS upper air temperature sounding channels (3–6) have relatively larger biases (less than 2.5 K) than the water vapor sounding channels. However, the standard deviation of the difference between observations and simulations (O-B) is larger for water vapor sounding channels. For ERA simulations, MWHS sounding channels exhibit negative biases similar to GNOS results but the standard deviation of O-B at the water vapor channels is much smaller. When compared with ATMS water vapor channels, MWHS biases are mostly negative and agree with those from ERA simulation. Thus, the large uncertainty in simulating MWHS water vapor sounding channels from GNOS could result from the poor input water vapor profiles and high water vapor variability in the lower troposphere.

Tropical cyclones typically exhibit warm cores aloft and their locations vary, depending on their development stages. In our recent studies, typhoon thermal structures can be derived reasonably well from satellite microwave sounding instruments such as AMSU and ATMS through better constraints of background profiles in 1dvar. The background profiles should be scene- dependent according to cloud type. It is found that the scene-dependent observation error covariances are also required to further improve the retrievals. With newly launched FY-3D satellite, MWHS data at 118 GHz are used to retrieve the atmospheric temperature and humidity profiles. Comparing to ATMS, MWHS can define the warm core locations very well although the warm core magnitude is weaker than that from ATMS.

Estimation of hurricane intensity is typically relying on aircraft observations and remote sensing data. This letter develops a satellite algorithm to estimate the hurricane surface maximum wind speed. The Advanced Technology Microwave Sounder (ATMS) observations are used to retrieve the atmospheric temperatures. The temperature anomaly field at each pressure level within the hurricane is derived with respect to its environment. It is shown that for a tropical cyclone entering the hurricane stage, a warm core anomaly occurs in upper troposphere. From the GPS dropsonde data of three hurricane cases (Harvey, Irma, and Maria) in 2017, a linear relationship is derived between the maximum warm core value and maximum surface wind speed. Thus, ATMS-derived warm core anomaly is a very good indicator of storm intensity and can be used in the operational weather forecasts.

Accurate global observations from space are critical for global climate change study. However, atmospheric temperature trend derived from spaceborne microwave instruments remains a subject of debate, due mainly to the uncertainty in characterizing the long-term drift of instrument calibration. Thus, a highly stable target with a well-known microwave radiation is required to evaluate the long-term calibration stability. This paper develops a new model to simulate the lunar emission at microwave frequencies, and the model is then used for monitoring the stability of the Advanced Technology Microwave Sounder (ATMS) onboard Suomi NPP satellite. It is shown that the ATMS cold space view of lunar radiation agrees well with the model simulation during the past five years and this instrument is capable of serving the reference instrument for atmospheric temperature trending studies, and connecting the previous generation of microwave sounders from NOAA-15 to the future Joint Polar Satellite System Microwave Sounder onboard NOAA-20 satellite.

Since early 2012, the Cross-track Infrared Sounder (CrI)S on board the Suomi National Polar-Orbiting Partnership (S-NPP) Satellite has continually provided the hyperspectral infrared observations for profiling atmospheric temperature, moisture and greenhouse gases. In this study, the CrIS Sensor Data Record (SDR) data are improved for climate applications with its fine-tuning of calibration coefficients in NOAA reprocessing project. One specific code for CrIS SDR reprocessing was developed. This code was updated with calibration algorithm, non-linearity, and geolocation to improve the scientific results. The calibration coefficients are refined with the latest updates based on the work from CrIS science team, and are inserted in the Engineering Packet (EP) in the Raw Data Record (RDR) data stream. The resampling wavelength was updated based on the metrology laser wavelength and resulting in zero sampling error in the spectral calibration. All the SDRs are generated with the same calibration coefficients, resulting in improved consistency during the CrIS lifetime mission. The reprocessed SDRs are essential for deriving the long-term climate trending such as tropical sea surface temperature trends and CO2trends. Evaluation of CrIS reprocessed SDR in term of spectral, and radiometric long-term accuracy and stability are presented.

This study describes the improved calibration applied for VIIRS SDR reprocessing. At reflective solar bands (RSBs), the earth-view F-factor is adjusted with the lunar observations and changes about 2% in band M2-M4 in past five years. An empirical method is applied to remove the radiance spikes in VIIRS thermal emissive bands (TEBs) during the blackbody Warm-Up-Cool-Down (WUCD). VIIRS DNB calibration is improved by applying continuous spectral response changes, new bias tables, updated gain ratio tables, stray light tables, and terrain corrections.

Accurate global observations from space are a critical part of the needed knowledge base for global climate change study. However, atmospheric temperature trends derived from space-borne microwave instruments remains a subject of debate, mainly due to uncertainty arise from calibration error and long-term drift and degradation of instrument. In this study, a new method was developed for assessing microwave radiometer long-term on-orbit calibration stability, by taking the lunar observation as a reference. This method was applied to 5 years of NPP ATMS lunar observations, results show that ATMS calibration accuracy and stability can be well assessed by using the Moon as a reference target.

Spaceborne radiometers have provided critical information of atmospheric water vapor and precipitation through sounding channels around 183 GHz. Understanding the performance and characteristics of these channels is important for hardware assessment, refining radiative transfer model, and improving science product. Whereas atmospheric water profiles from ancillary operational or reanalysis data are widely used for RTM simulation of 183 GHz, they cannot well represent the real atmospheric conditions due to the intrinsic nature of water/precipitation heterogeneity and variability. In addition, these ancillary data are with limited spatial (~100 km) and temporal (several hours) resolution. As a result, significant errors propagate into simulation. The combined radar and radiometer of GPM core observatory measure both vertical profile and total column radiance and therefore can well retrieve water profiles with fine spatial resolution and instaneous temporal resolution. We have developed a scheme using the combined retrieval product for simulating ATMS radiance. A rigorous simulation is performed with considering the field of view matchup, variation of beam size along cross-track scanning, radiometer spectral response, and antenna pattern convolution. It is shown that the simulated brightness temperature agree well with observation. The scheme can combine DPR/GMI product and different radiometers for instrument assessment and model refinement.

Accurate representation of currently actual land surface types at regional to global scales is critical for land surface parameterization in numerical weather, climate, hydrological and ecological models. Depending on the time scale, monitoring land surface type changes is also increasingly important for natural disaster assessment and natural resources management. To provide near real time surface type information for downstream data product generation (e.g. land surface temperature) from the Suomi-National Polar-orbiting Partnership (S-NPP) Visible and Infrared Radiometer Suite (VIIRS), numerical weather prediction models, and natural disaster assessment and resources management, the Surface Type (ST) Environmental Data Record (EDR) has been generated since early 2012 from the VIIRS observations. The ST EDR contains a static surface type label as well as current day active fire and snow conditions for each 750m pixel of the whole globe. In addition to the surface type changes caused by active fire and snow, other surface type changes may occur and have implications for land surface parameterization. For example, flooding of a land area will completely change its surface albedo, temperature, and the water, energy and CO2exchange rates. In this study, a near real time surface type change monitoring framework is developed and tested to provide daily surface type information using the S-NPP and future Joint Polar Satellite System (JPSS) VIIRS surface reflectance observations. This near real time surface type information includes burned, flooded, deforested/urbanized, crop-harvested areas in addition to the surface type label described by a static surface type map generated from annual or multi-year satellite observations. In this paper, the current VIIRS surface type EDR and its characteristics are introduced as reference. The design of a surface type change monitoring framework and the surface type change detection methodology are described, followed by three case studies in which this framework is used to detect flooding and burned areas. The potential for making the framework operational for use in operations of numerical weather prediction, water resources and disaster management is also discussed.

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument which is onboard the Suomi National Polar-orbiting Partnership (S-NPP)/Joint Polar Satellite System (JPSS) satellites has transitioned much of the capability of the experimental MODerate Resolution Imaging Spectroradiometer (MODIS) instruments into the operational domain. VIIRS provides a continuation of global environment monitoring for Land, Ocean, Cloud, and Atmosphere. The high quality observations and derived products generated from VIIRS have been used to improve operational environmental forecast skills and enhance our understanding of climate change processes. Since S-NPP successfully launched in October 2011, NOAA STAR science teams have been focused on maintaining, development, calibration/validation, and upgrade of the VIIRS algorithms. By far, most of the S-NPP VIIRS sensor and environmental data products have been fully validated and characterized through rigorous cal/val review process. The validated science algorithms are now used for reprocessing of the entire S-NPP mission lifetime of the VIIRS data product records. This paper presents an insight into these processes with examples of science data products operationally available from the S-NPP VIIRS Instrument. In addition, plans and preparations towards JPSS-1 launch and pre-operational evaluation JPSS-1 algorithms and product performance using proxy and Thermal Vacuum measurements are presented.

Recently, the advanced coastline inflection point method (CIPSD) was developed to retrieve the instrument boresight pointing error of Suomi National Polar-orbiting Partnership (SNPP) Advanced Technology Microwave Sounder (ATMS). Because of the use of mathematical model and separate-domain technique, this algorithm can retrieve the boresight pointing error in terms of Euler angles efficiently. But the retrieval uncertainty of yaw is larger than that of the other two angles due to the limitation in this method. This paper thoroughly investigates the phenomenon of asynchrony between the serial of space view counts and that of the simulated antenna response in lunar intrusion (LI) events. Sensitivity study shows that this asynchrony is mainly caused by the boresight pointing Euler angle yaw. A new retrieval algorithm for yaw is developed based on this relation. The yaw derived from the Lunar observations of space view 4 (SPV4) from 2011 to 2016 is 0.028° at K band, -0.012° at Ka band, -0.168° at V band, -0.005° at W band, and -0.054 at G band. This algorithm can serve as a supplement to CIPSD. The combined retrieval results, the roll and pitch from CIPSD and the yaw from lunar observations, are used to correct the ATMS SPV vectors when simulating the antenna response. The comparison between the corrected antenna response and the observed cold counts of ATMS channel 7 SPV4 at the LI event occurring on Jan 6-8, 2017 shows that, after correction, the simulated antenna response synchronizes very well with the actual observations.

The launches of the US Advanced Baseline Imager (ABI) on November 19, 2016 and the Japanese Advanced Himawari Imager (AHI) on October 7, 2014 represented a new era of geostationary operational environmental satellite (GOES) imagers. Both ABI and AHI provide many more channels than any other already launched GOES imagers in the world. In this study, we compare the impacts of assimilating all ABI and/or AHI versus GOES-like infrared channels radiances on regional quantitative precipitation forecasts (QPFs) over US and China, respectively. Positive impacts on QPFs over land are obtained for a typical summer precipitation case by assimilating either all ten AHI infrared channels (AHI-10) or only four GOES-like AHI channels (AHI-4) when compared with a control experiment without assimilating GOES observations (CTRL). It is found that a southwest to northeast oriented band of the atmosphere with high water vapor content that was formed and moved inland with time under the influence of a subtropical high and an eastward propagating middle-latitude trough was responsible for the persistent precipitation in the eastern China of the selected case. The AHI-10 experiment generated larger improvements on QPFs due to it generating a wetter atmosphere in the middle and low troposphere over the ocean off the southeast coast of China than the AHI-4 and CTRL experiments.

Since early 2012, the cross-track infrared sounder (CrIS) onboard the Suomi National Polar-Orbiting Partnership satellite has continually provided the hyperspectral infrared observations for profiling atmospheric temperature, moisture, and greenhouse gases. The CrIS radiance data are also directly assimilated into global numerical weather prediction models to improve the medium-range forecasts. These important applications require accurate CrIS calibration. Since CrIS radiometric accuracy depends on the accurate spectral calibration, it is important to reduce the spectral uncertainty and increase the calibration stability for weather and climate applications. In this paper, the accuracy of CrIS spectral calibration and its stability are assessed using the operational sensor data record (SDR) data generated by the interface data processing segment (IDPS). A spectral validation method is developed and applied to clear scene data over ocean from September 22, 2012, to April 19, 2016. It is shown that CrIS metrology laser wavelength varies within 3 ppm, as measured by the Neon calibration subsystem. While the current CrIS operational algorithm is designed to have a spectra error less than 2 ppm, the actual spectral errors are about 4 ppm due to the IDPS software bugs. A new correction method is applied to fix the bugs and to further improve CrIS spectral calibration. It is found that the CrIS spectral calibration accuracy is less than 1 ppm in both normal and full spectral resolution SDR data sets.

The Ozone Mapping and Profiler Suite (OMPS) is carried onboard on the Suomi National Polar-orbiting Partnership satellite which was launched on October 28, 2011. Since its launch, many changes in radiometric and spectrometric calibration have been made to improve the OMPS sensor data quality. The most challenging issue is to correct an unexpected variation of the in-flight wavelength scale in the OMPS Nadir Mapper (NM) spectrometer. Validation of the NM earth viewing albedo estimates of 2% to 5% wavelength-dependent errors across the sensor spatial instantaneous field of views (IFOVs). The root cause attributes these errors to a large variation in the wavelength registration for each of the NM charge-coupled device earth view pixels. Recent calibration change has significantly improved the total wavelength knowledge accuracy, and as a result the NM wavelength-dependent albedo uncertainty is reduced below the requirement of 2% for most of the channels across all of the spatial IFOVs.

The key calibration parameters important to instrument health and safety of Ozone Mapping Profiler Suite (OMPS) Nadir Sensors on board Suomi NPP (Suomi National Polar-orbiting Partnership) are being monitored since November 2011 at NOAA/STAR by the Intensive Calibration and Validation System (ICVA). OMPS has two instrument modules: a combined Nadir Mapper (NM) and Nadir Profiler (NP), and a separate Limb Profiler (LP). Both nadir sensors are designed to make measurements of the ultraviolet radiance backscattered by the Earth's atmosphere and surface and of the extra-terrestrial solar irradiance. In this paper, the trending of the OMPS key calibration parameters in the past four years is shown. The impacts of these parameters on OMPS earth view radiance and albedo for nadir sensors are analyzed.

This paper presents the in-flight charge-coupled device (CCD) dark characterization of the Suomi National Polar-orbiting Partnership (S-NPP) Ozone Mapping Profiler Suite (OMPS). Data from OMPS's three different CCD detector arrays have been collected to characterize in-flight detector behaviors. It is of our primary interest in the evolution and trend of the dark current as well as the signal distribution beyond the first 4 years of the mission. Based on in-situ measurements obtained during the prelaunch calibration, we monitor changes in the CCD dark current on the pixel level in order to validate the in-flight OMPS dark calibration. The dark current change along with the Random Telegraph Signal (RTS) and the South Atlantic Anomaly (SAA) are studied while focusing on the influence of the Sensor Data Record's quality in terms of radiance errors. Our results show that current in-flight dark calibration provides reasonable Sensor Data Records (SDRs) and Environmental Data Records (EDRs) with an error of less than ∼0.1% on average in the earth-view radiance. However, calibration error inside of the SAA region of influence due to transients is sizable for wavelengths less than 302 nm where the signal-to-noise ratio is low.

A new constellation comprising of eight microwave small satellites is proposed in this study. This constellation is capable of covering the entire globe every two hours. With six more satellites added and properly arranged, a constellation is able to provide hourly observations of fast-evolving severe weather systems like hurricanes. Compared to current polar-orbiting satellite which normally carries one passive microwave instrument onboard, a small satellite constellation is more cost-effective, requires a shorter development cycle and has smaller failure impact. NOAA Center for Satellite Applications and Research (STAR) has built a full radiance transformation system (ARTS) that is applicable for small satellite calibration, validation and data processing. Hourly NWP forecast fields for tropical storm Debby (2012) will be produced and used as inputs to Community Radiative Transfer Model (CRTM) to simulate the designed microwave small satellite observations to demonstrate their values for monitoring and predicting hurricane and typhoon events.

Satellite data assimilation for societal benefits is demonstrated using high quality ATMS and CrIS radiance data for improving forecast skill of landfalling hurricanes and typhoons. A comprehensive SDR (CSDR) from ATMS, CrIS and VIIRS is created and allows for maximizing the benefits to numerical weather prediction data assimilation. The success of satellite data assimilation requires various quality control techniques that are readily achieved with the new JPSS CSDR data streams.

The Earth-scene brightness temperature (TB) seen by satellite radiometers is generally cold over the ocean due to low emissivity, warm over land with high emissivity, and with a large dynamic range dependent on frequency and polarization. Calibration at either cold or warm end cannot fully characterize the calibration dependence on the full TB dynamic range. We have developed calibration methods using both warm and cold reference TB tie points and applied them to the Global Precipitation Measurement (GPM) mission particularly for the GPM Microwave Imager (GMI). The two-end method characterizes the GMI calibration dependence on TB and enables the development of TB-dependent intercalibration correction tables for GPM.

NASA Soil Moisture Active/Passive (SMAP) satellite was launched on January 31st, 2015 and has been providing level-1B radiometer brightness temperature (L1B-TB) data with an official latency of 12 hours since April 2015. The primary application users of the SMAP radiometer observations include numerical weather prediction operations that require shorter data latency (e.g. less than 6 hours). With slightly simplified algorithms of the L1B-TB data, NASA SMAP project is also providing near real time (NRT) L1B-TB data to NOAA operational users with a latency ranging from 2-6 hours. With this latency NOAA NWP models could use most of those SMAP half-orbit data that arrives within the 6 hour cut-off time limit. Before a radiance data assimilation capability for NOAA NWP models is developed, NOAA NESDIS is retrieving soil moisture from the NRT L1B-TB data through its Soil Moisture Product System (SMOPS) and makes surface soil moisture directly available for assimilation into the NWP models with the shortest possible turn-around time. Unlike using NASA GEOS model surface and soil temperature and multi-year average vegetation index data, SMOPS soil moisture retrieval algorithm uses the surface temperature data from operational Global Forecast System (GFS) and the vegetation index (NDVI) data from the near real time Suomi-NPP VIIRS observations. This paper introduce the structure of SMOPS, the soil moisture retrieval algorithm for SMAP data, and validation of the soil moisture data products against in the situ soil moisture measurements collected from the Soil Climate Analysis Networks of US Department of Agriculture and the Tibetan Soil Moisture Networks of Chinese Academy of Sciences. The soil moisture data retrieved from SMAP NRT TB data through SMOPS are also evaluated against the NASA official SMAP Level 2/3 soil moisture products. Preliminary validation results indicate that the SMAP soil moisture product from the SMAP NRT TB data and NESDIS SMOPS has similar quality to the NASA official products, but has shorter latency which may be critical to NOAA NWP operations.

Today's rapidly increasing environment satellite observations brought unprecedented opportunities for monitoring the earth environments with remote sensing measurement. The Joint Polar Satellite System (JPSS) is the National Oceanic and Atmospheric Administration's (NOAA) next generation polar satellite program that provides global environmental remote sensing of weather, climate and other environmental applications. The Suomi National Polar-orbiting Partnership (S-NPP) satellite launched in October 2011 was the first satellite designed to bridge into the future JPSS constellation. The data products produced from the S-NPP/JPSS satellites provide critical observations for accurate weather forecasting, reliable severe storm outlooks, and global measurements of atmospheric and oceanic conditions such as sea surface temperatures, ocean color, ozone and other trace gases, aerosol, clouds, temperature and moisture profiles, wind speeds, land surface properties, snow and ice covers, etc. S-NPP/JPSS products provides critical support to NOAA's ecosystems, climate, weather, and water mission goals through numerous operational applications, including weather and climate modeling and prediction, fisheries management; ecosystem monitoring and management; and understanding ocean dynamics and climate variability. This paper will provide insight into the Suomi National Polar-orbiting Partnership (S-NPP) data products and their applications for environment monitoring. Specifically, the presentation will be focused on the advanced data products derived from the Cross-track Infrared Sounder (CrIS) measurements, Cal/Val status, products performance, and their applications for monitoring the atmospheric variability.

Accurate instrument mounting matrix information is significant for geolocation algorithm to reach a high on orbit geolocation accuracy. In this study, an integrated satellite-ground geolocation error model was proposed, based on which the instrument mounting matrix can be accurately rebuilt. This method has been successfully applied to SNPP ATMS on-orbit geolocation, results show that the geolocation error can be reduced by 34%.

Soil surface roughness is a crucial factor affecting the land surface microwave emissivity. Presented in this paper is a semiempirical model that analytically accounts for both roughness attenuation and cross-polarization-mixing effects in the frequency range of 1-100 GHz. The model is based on the finite linear superposition of hyperbolic tangent (tanh) functions over the normalized surface roughness and radiative parameter space, which proves to be very flexible and efficient in handling the distinct asymptotic features of roughness effects at the low-frequency end and high-frequency end and the nonlinear structure in between. The model performance was analyzed with the ground-based reflectivity measurements collected from different sources. In comparison with the existing semiempirical models in the literature, the new tanh-based roughness model demonstrated higher accuracy and consistent performance in the frequency range of 1.4-100 GHz and 0° ~ 60° incident angles.

Microwave Radiation Imager (MWRI) onboard the FengYun (FY)-3C satellite provides measurements of the Earth's atmosphere and surface at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz with dual polarization. While FY MWRI data have been widely distributed to the user community, their geolocation accuracy has not been documented. In this paper, the coastline inflection method is used to estimate MWRI geolocation errors. Three coastal regions where MWRI brightness temperature exhibits a large contrast are selected for the geolocation analysis. A total of 720 MWRI data points are identified that cross the coastlines. The latitudes and longitudes at these data points are compared with the fine-resolution database of the Global Self-consistent, Hierarchical, High-resolution Shoreline (GSHHS). It is found that the mean geolocation errors in along- and cross-track directions are approximately 5-6 km at 89 GHz. This magnitude of errors is more than 30% of the field-of-view size at 89 GHz. Such a geolocation error must be corrected so that the MWRI data can be more useful for quantitative remote sensing. Thus, the mean geolocation errors are further utilized to adjust the satellite attitude angles (e.g., pitch, roll, and raw). After the attitude angle correction, the MWRI geolocation is very accurate at 89 GHz, and errors in other MWRI channels may be corrected through their co-registration relationships to the 89-GHz channel.

The cold calibration count from the Advanced Technology Microwave Sounder (ATMS) space view increases when the lunar radiation intrudes its antenna field of view (FOV). This increase is referred to as lunar contamination since the cold count is not matched with the specified brightness temperature of 2.73 K. For ATMS, it is found that the elapse time of lunar intrusion (LI) and the magnitude of the cold count increase are channel dependent. If the lunar-affected calibration counts are rejected in the processing, a data gap can be shown in brightness temperature at all channels. At ATMS channels 1 and 2, which have a large FOV, the LI can result in an increase of 40 counts in cold calibration. At higher frequency channels which have a smaller FOV size, the LI intensity is much stronger and can be as large as a few hundred counts. The LI becomes significant when its radiation appears in the ATMS antenna main beam. In the current ATMS operational calibration algorithm, the cold count anomaly is detected when the intensity of LI exceeds a certain threshold. The lunar radiation can be also corrected in the ATMS calibration. In doing so, a lunar radiation term is derived as a function of antenna gain, the solid angle of the Moon, and the brightness temperature of the Moon disk. This algorithm is applied in an ATMS calibration system developed at NOAA and shows a successful removal of all the lunar contamination on the earth-scene brightness temperature.

The Advanced Technology Microwave Sounder (ATMS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite is a total power radiometer and scans across the track with a range of ±52.77° from nadir. It has 22 channels and measures the microwave radiation at either quasi-vertical or quasi-horizontal polarization from the Earth's atmosphere. The ATMS scanning reflector is made of beryllium coated with gold and can have an emission due to the surface roughness. During prelaunch phase, an estimate of the reflector emissivity was not explored. In this paper, a new methodology is developed to assess the antenna emission from the ATMS pitch-over observations. It is found that the antenna emission is significant and dominates the scan-angle-dependent features in the ATMS antenna temperatures. Retrieved emissivity from K- to G-bands ranges from 0.002 to 0.006. An error model was also developed to assess the impact of antenna emissivity to calibration accuracy of antenna temperature products. Simulation results show that the calibration error is scene temperature dependent and can be as large as 2.5 K for space view.

This paper presents a comparison of methane (CH4), one important greenhouse gas in terrestrial atmosphere, observed from two hyperspectral infrared sensors on satellites, one is the Atmospheric Infrared Sounder (AIRS) onboard NASA/AQUA and the other is the Infrared Atmospheric Sounding Interferometer (IASI) onboard METEOP-A and -B. Comparison using about 500 cases shows the mean DOFs from AIRS is slightly smaller than IASI, with the difference of -0.049±0.152. Overall the retrieved CH4from AIRS is larger than IASI, and their difference is 10.2±23.8 ppb below 650 hpa and 27.4±26.9 ppb above 350 hPa. In the most sensitive layer of infrared sounder between 350 and 650 hPa, their difference is as small as 2.8±17.2 ppb. Compared to aircraft measurements, AIRS retrievals tend to be overestimated while IASI retrievals tend to be underestimated in most cases, suggesting the necessarity to further improve the algorithms and call for more dedicated aircraft measurements for validation.

The Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-orbiting Partnership (NPP) satellite is a total power radiometer and scans across the track within a range of ±52.77° from nadir. It has 22 channels and measures the microwave radiation at either quasi-vertical or quasi-horizontal polarization from the Earth's atmosphere. From the ATMS pitch-over deep space scan observations, it is found that the antenna reflector losses play an important role in calibration and dominates the scan angle dependent features in the ATMS antenna temperatures. Since the losses are small, they are difficult to measure by traditional means. However, they can be assessed directly from pitch over observations by using deep space radiation measured at different scan angle. This paper describes a physical model developed for the correction of reflector emissivity, which incorporates the angular dependent terms derived from the pitch-over maneuver data. Based on the pitch-maneuver data, the expected V/H polarization emissivity value is in the range of 0.002 to 0.0065. Considering the facts that ATMS is heritage of historical NOAA series microwave sounding instruments like MSU and AMSU, on which the reflector emissivity correction model can also be used to improve TDR/SDR data quality.

Currently, the instrument sensitivity of sensors onboard weather satellites is quantified by computing the standard deviation of the measurements taken from their calibration targets. The standard deviation is valid for describing the spread of a statistical distribution of the measured values around its mean that is stable. However, the actual measurements of a calibration target can exhibit considerable variations in time as shown from the Suomi National Polar-orbiting Partnership Advanced Technology Microwave Sounder (ATMS) blackbody data. In this letter, the Allan deviation is proposed as an alternative to the standard deviation for characterizing the instrument sensitivity. It is found that, in the overlapping Allan deviation formula, the averaging window size has to be set to one in order to accurately assess the noise magnitudes for both stationary and nonstationary time series. Furthermore, from the ATMS on-orbit data, the estimates of the noise magnitudes at several channels show a large discrepancy between the Allan deviation and the standard deviation. Finally applying the Allan deviation, the sensitivity of the NOAA-18 Advanced Microwave Sounding Unit-A is also derived and compared against the traditional algorithm results. From this comparison, significant improvements can be seen in the Allan deviation-based noise-equivalent-differential-temperature estimation.

The intercomparison of MetOp-A and MetOp-B Advanced Very High Resolution Radiometer visible and near-infrared (NIR) channels over the Libyan Desert with consideration of the effect from the bidirectional reflectance distribution function (BRDF) is used for MetOp-B postlaunch calibration update. In order to remove this effect from the comparison, two methods are investigated: BRDF effect modeling in direct comparison and seasonal oscillation regression. The BRDF modeling method employs both a simple linear model and an empirical model. The seasonal oscillation method has improved by adding a curve regression, where the seasonal oscillation is derived from MetOp-A measurement data since its launch and used for the regression of MetOp-B measurements. Using these methods, the reflectance ratios of MetOp-B over MetOp-A in three reflective solar channels have been derived, which are 1.034 for channel 1, 0.912 for channel 2, and 0.805 for channel 3A. The uncertainties of the derived ratios are estimated in the range of 4.7%-6.4%. The model accuracy and uncertainty have been discussed. Initial calibration updates based on these results have been delivered for the MetOp-B operational L1B product, and a routine update is performed monthly. The methods used in this work are also applicable to the intercomparison of other visible and NIR instruments.

This paper evaluates the radiometric accuracy of observations from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership and Sondeur Atmospherique du Profil d' Humidité Intropicale par Radiométrie (SAPHIR) onboard Megha-Tropiques through intercalibration and validation versus in situ radiosonde and Global Positioning System Radio Occultation (GPS-RO) observations. SAPHIR and ATMS water vapor channels operate at slightly different frequencies. We calculated the bias due to radiometric errors as the difference between the observed and simulated differences between the two instruments. This difference, which is often referred to as double difference, ranges between 0.3 and 0.7 K, which shows good consistency between the instruments. We used a radiative transfer model to simulate the satellite brightness temperatures (Tbs) using radiosonde and GPS-RO profiles and then compared simulated and observed Tbs. The difference between radiosonde and ATMS Tbs for the middle and upper tropospheric temperature sounding channels was less than 0.5 K at most stations, but the difference between radiosonde and ATMS/SAPHIR Tbs for water vapor channels was between 0.5 and 2.0 K. The larger bias for the water vapor channels is mainly due to several errors in radiosonde humidity observations. The mean differences between the ATMS observations and the Tbs simulated using GPS-RO profiles were 0.2, 0.3, 0.4, 0.2, and -0.2 K for channels 10-14, respectively; and the uncertainty increases from 0.02 K for channel 10 to 0.07 K for channel 14.

In this paper we evaluate the potential of assimilation of Cross-track Infrared Sounder (CrIS) radiance in the warm start HWRF system for Hurricane Sandy forecast using collocated Visible Infrared Imaging Radiometer Suite (VIIRS) cloud product for cloud detection and CrIS channels clearing. We examine the CrIS data bias correction and quality control procedure in GSI. Then we compare the cloud parameters retrieved from GSI stand-alone algorithm with those from CrIS/VIIRS collocated cloud products. And the impact of applying CrIS/VIIRS collocated cloud products on CrIS quality control in GSI has been evaluated.

The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key environmental remote-sensing instruments onboard the Suomi National Polar-Orbiting Partnership spacecraft, which was successfully launched on October 28, 2011 from the Vandenberg Air Force Base, California. Following a series of spacecraft and sensor activation operations, the VIIRS nadir door was opened on November 21, 2011. The first VIIRS image acquired signifies a new generation of operational moderate resolution-imaging capabilities following the legacy of the advanced very high-resolution radiometer series on NOAA satellites and Terra and Aqua Moderate-Resolution Imaging Spectroradiometer for NASA's Earth Observing system. VIIRS provides significant enhancements to the operational environmental monitoring and numerical weather forecasting, with 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns, providing the sensor data records for 23 environmental data records including aerosol, cloud properties, fire, albedo, snow and ice, vegetation, sea surface temperature, ocean color, and nigh-time visible-light-related applications. Preliminary results from the on-orbit verification in the postlaunch check-out and intensive calibration and validation have shown that VIIRS is performing well and producing high-quality images. This paper provides an overview of the on-orbit performance of VIIRS, the calibration/validation (cal/val) activities and methodologies used. It presents an assessment of the sensor initial on-orbit calibration and performance based on the efforts from the VIIRS-SDR team. Known anomalies, issues, and future calibration efforts, including the long-term monitoring, and intercalibration are also discussed.

The absolute accuracy of antenna brightness temperatures (TDR) from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-orbiting Partnership satellite is estimated using the Constellation Observing System for Meteorology, Ionosphere, and Climate radio occultation (RO) data as input to the U.S. Joint Center of Satellite Data Assimilation community radiative transfer model (RTF). It is found that the mean differences (e.g., biases) of observed TDR observations to GPS RO simulations are positive for channels 6, 10–13 with values smaller than 0.5 K and negative for channels 5, 7–9 with values greater than${-}{0.7}~{\rm K}$. The bias distribution is slightly asymmetric across the scan line. A line-by-line RTF is used to further understand the sources of errors in forward calculations. It is found that, for some channels, the bias can be further reduced in a magnitude of 0.3 K if the accurate line-by-line simulations are used. With the high quality of GPS RO observations and the accurate RTF, ATMS upper level temperature sounding channels are calibrated with known absolute accuracy. After the bias removal in ATMS TDR data, it is shown that the distribution of residual errors for ATMS channels 5–13 is close to a normal Gaussian one. Thus, for these channels, the ATMS antenna brightness temperature can be absolutely calibrated to the sensor brightness temperature without a systematic bias.

The Ozone Mapping and Profiler Suite (OMPS), launched on-board the Suomi National Polar-orbiting Partnership Spacecraft (S-NPP) on October 28, 2011, has been in operation for more than one year. It continues 40 years ozone study missions by providing the S-NPP science community with data products of ozone in the Earth's atmosphere. The sensor as well as the associated mission data processing system, the Interface Data Processing Segment (IDPS), necessary to process the Raw Data Records (RDRs) into Sensor Data Records (SDRs) and Environmental Data Records (EDRs), has been extensively calibrated and evaluated during the early orbit check and intensive calibration and validation (ICV). This paper will summarize the provisional SDR performance as well as the modifications of the IDPS SDR algorithm based on needs identified during the EOC and ICV. Examples of the first 18 month's sensor calibration parameters trending will be provided.

The Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-orbiting Partnership (NPP) satellite is a total power radiometer and scans across the track within a range of$\pm 52.77^{\circ}$from nadir. It has 22 channels and measures the microwave radiation at either quasi-vertical or quasi-horizontal polarization from the Earth's atmosphere. Without simultaneous measurements at both polarizations at the same frequency, the conversion from ATMS antenna temperature to sensor brightness temperature becomes nonunique if the antenna subsystem has a significant spillover from cross-polarization. In addition, the antenna temperature could be contributed from both the near- and far-field radiation through the sidelobes of the ATMS antenna subsystem. An analysis of the ATMS antenna gain measurements reveals that the efficiencies of both ATMS antenna sidelobes and cross-polarization are frequency dependent. From the ATMS pitchover maneuver data, it is found that the contributions of spacecraft radiation through the near-field sidelobes are significant and dominates the scan-angle-dependent features in the ATMS antenna temperatures. A theoretical model is developed for the conversion from antenna to sensor brightness temperatures, which incorporates the angular dependent terms derived from the pitchover maneuver data.

Hyperspectral radiances from the Infrared Atmospheric Sounding Interferometer (IASI) and Atmospheric Infrared Sounder (AIRS) are used as a reference to improve the calibration accuracy for FengYun-2 (FY-2) infrared (IR) channel radiances. It is shown that the previous FY-2 operational calibration for IR bands produces significant bias in brightness temperatures that can exceed 1.1 K. In particular, the FY-2 IR3 band (6.7 μm) has the largest bias of 2.0 K. The daytime double-difference temperature (DDT) between AIRS and IASI using FY-2 imagers as a transfer medium showed an excellent consistency, is within 0.2 K at 290 K, and is stable over time for FY-2C/2D/2E. This only indicates the robust calibrations applied for both the AIRS and IASI measurements. During the nighttime of the Earth observation, stray light in space affects the long-term stability of the FY-2 DDT, particularly for the Earth scene at 220 K. FY-2E satellite which was launched in 2009 has an instrument design improvement. Intercalibrating FY-2 four times using AIRS and IASI data can reveal the diurnal features of the FY-2 instrument calibration. The temporal DDT appears very large during the spring and autumn eclipse times. Not only can the global-space-based-intercalibration-system intercalibration method provide an excellent operational calibration for the FY-2 imager, but it can also help improve the design of future instruments and onboard blackbody calibration.

More precise and accurate geostationary measurements are highly needed for satellite applications. It was well known that the Geostationary Operational Environmental Satellite (GOES)-12 imager was susceptible to water-ice contamination, and thus, several decontamination efforts were carried out to remove built-up ice on the instrument during operation. The intercalibration results of GOES-12 with the Atmospheric Infrared (IR) Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI) indicate that the calibration accuracy of GOES-12 was impacted by the decontamination procedures. Relative to the AIRS and the IASI, the GOES-12 imager radiances or brightness temperatures increased in the CO2sounding channel (channel 6, 13.3 μm) and decreased in the water-vapor absorption channel (channel 3, 6.5 μm) but was less changed in the window channel (channel 4, 10.7 μm). A simple conceptual model is then proposed to give a physical explanation on the different behaviors of three IR channels in response to the ice-removal procedures.

A detection of radio-frequency interference (RFI) in the space-borne microwave radiometer data is difficult under snow and sea ice-covered conditions. The existing methods such as a spectral difference technique or a principal component analysis (PCA) of RFI indices produce many false RFI signals near the boundary of Greenland and Antarctic ice sheets. In this paper, a double PCA (DPCA) method is developed for RFI detection over Greenland and Antarctic regions. It is shown that the new DPCA method is effective in detecting RFI signals in the C- and X-band radiometer channels of WindSat while removing the false RFI signals over Greenland and Antarctic. It also worked well in other snow-free or snow-rich regions such as winter data over the United States. The proposed DPCA can be applied to satellite radiometer data orbit-by-orbit or granule-by-granule and is thus applicable in an operational environment for fast processing and data dissemination.

The Defense Meteorological Satellite Program's (DMSP) F-18 Special Sensor Microwave Imager/Sounder (SSMIS) brightness temperature difference (O-B) between observations and simulations from the Community Radiative Transfer Model (CRTM) based on 6 hour forecast fields of the National Centers for Environmental Prediction (NCEP) Global Forest System (GFS) are analyzed in this paper. It shows that after the current GSI bias correction, the O-B from SSMIS F-18 lower atmospheric sounding (LAS) channels are still significant and depend on nodes, seasons, latitudes and channels.

VIIRS thermal emissive bands (TEB) have been analyzed. The analysis results indicate the VIIRS TEB is stable and exceeds the specification. Comparisons between the VIIRS and other sensors such as AVHRR, MODIS and CrIS demonstrated that VIIRS TEB agrees well with those sensors. Using Community Radiative Transfer Model (CRTM), developed at U.S. Joint Center for Satellite Data Assimilation (JCSDA), we found that M12 band image striping at day time over a uniform ocean was caused by the difference of sensor azimuthal angles among detectors.

The Community Radiative Transfer Model (CRTM), developed at U.S. Joint Center for Satellite Data Assimilation (JCSDA), has been used for infrared and microwave satellite radiance simulations and their derivatives to the surface/atmospheric parameters in data assimilation, physical retrieval, and many others. The CRTM has also been applied visible sensors and visible channel radiance/reflectance is simulated for studying aerosol and cloud effect. Together with the CRTM aerosol optical depth (AOD) module, the MODIS AOD satellite products can be assimilated and enhance air quality forecasting. This paper gives an overview of the CRTM developments and functionalities as well as its applications.

The dark signal on an Ozone Mapper Profiler Suite (OMPS) charge-coupled device (CCD) results when electrons are thermally emitted into the conduction band of pixels in the active and storage regions. It effectively adds offsets to the photon-generated pixel counts and therefore impacts the Sensor Data Record (SDR) performance. This paper presents OMPS on-orbit results from dark current and electrical bias characterization and calibration on the system level. In particular, data from nominal and diagnostic activities has been collected and analyzed to extract general trends and features about the in-flight detector behaviors. The in-flight dark and bias signals are then compared with the prelaunch on-ground performance for each channel. The South Atlantic Anomaly (SAA) impact and temperature dependency are also addressed. The analysis results have demonstrated that the OMPS CCD performance successfully transferred from ground to orbit; the in-flight timing pattern of the nominal dark measurements are well suited to determine general dark currents of the individual pixels, and data collected during the early orbit calibration are sufficient to perform internal consistency checks of sensor dark and bias parameters and instrument behavior. Results also suggest that to minimize the influence of hot pixels and other effects of CCD lattice damage due to energetic particle hits, the dark current shall be updated on a daily basis rather than the weekly basis as planned.

ATMS cross-track asymmetric pattern is investigated in this study. It is found that the asymmetric biases are close to the simulations when the polarization alignment angles are set between 91° and 92° for the three window channels. A new algorithm is developed to retrieve atmospheric temperature profiles for tropical cyclone with ATMS data. The algorithm is applied for Tropical Cyclone Giovanna case study and compared with the retrievals from NOAA-15 AMSU-A observation. ATMS retrievals clearly depict the cold temperature anomalies in TC spiral rain bands and the storm warm core.

The MicroWave Temperature Sounder (MWTS) on FY-3A has four channels with designed band central frequencies of 50.3, 53.6, 54.9, and 57.3 GHz, respectively. Lu et al. found that the central frequency for three upper level sounding channels shifted after the satellite launch into orbit. This study confirms the findings Lu et al. using a different numerical weather prediction (NWP) model and a different radiative transfer model. Furthermore, it is shown that the strong temperature dependence of MWTSO-BDFbiases found in our earlier work is mostly induced by these frequency shifts, where O represents MWTS observations andBDFis model simulations. The mean difference of brightness temperature simulations with (BSF) and without (BSF) incorporating the frequency shifts into the radiative transfer model resembles theO-BSFbiases. For NWP applications of FY-3A MWTS data, it is sufficient to generate new fast radiative transfer model coefficients that incorporate the new passband parameters, and the resulting MWTSO-Bshiftedbiases become constant as those of MetOp-A/NOAA-18 AMSU-A data. For climate applications, the FY-3A MWTS brightness temperatures adjusted by subtractingBSF-BDFmatch quite well with the MetOp-A/NOAA-18 AMSU-A data at the simultaneous nadir overpass locations in both the Arctic and Antarctic.

The 15 papers in this special issue focus on the Chinese Fengyun (FY)-3 Satellite instrument calibration and applications.

The MicroWave Radiation Imager (MWRI) onboard the FengYun (FY)-3B satellite has five frequencies at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz, each having dual channels at vertical and horizontal polarization states, respectively. It is found that radio-frequency interference (RFI) is present in MWRI data over land. The RFI signals are, in general, detectable from a spectral difference method and a principal component analysis (PCA) method. In particular, the PCA method is applied to derive RFI signals from natural radiations by using the characteristics of natural radiation measurements having all-channel correlations. In the area where data have a higher projection onto the first principle component (PC) mode, RFI is, in general, present. However, both the spectral and PCA methods cannot detect RFI reliably over frozen grounds and scattering surfaces, where the brightness temperature difference between 10.65 and 18.7 GHz is large. Thus, detection is improved through the use of normalized PCA. The new RFI detection algorithm is now working reliably for MWRI applications. It is found that RFI at 10.65 GHz distributes widely over Europe and Japan, and is less popular over the United States and China.

Brightness temperatures from NOAA-18 and NOAA-19 MHS measurements are investigated over Antarctica to establish it as a natural vicarious calibration target for determination of the Inter-satellite/inter-sensor Calibration Biases (ICB) of the two MHS radiometers. Establishment of a natural test site for calibration references is important for calibration and validation of space-borne microwave instruments.

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

A comprehensive system that is used to invert the geophysical products from microwave measurements has recently been developed. This system, known as the Microwave Integrated Retrieval System (MiRS), ensures that the final solution is consistent with the measurements and, when used as input to the forward operator, fits them to within the instrument noise levels. In the presence of precipitation, this variational algorithm retrieves a set of hydrometeor products consisting of cloud liquid water, ice water, and rain water content profiles. This paper presents the development and assessment of the MiRS rainfall rate that is derived based on a predetermined relationship of the rainfall with these hydrometeor products. Since this relationship relies on the geophysical products retrieved by the MiRS as inputs and not on sensor-dependent parameters, the technique is suitable for all microwave sensors to which the MiRS is applied. This precipitation technique has been designed to facilitate its transition from research to operations when applied to current and future satellite-based sensors. Currently, the MiRS rainfall rate technique has been implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) for the NOAA-18, NOAA-19, Metop-A Advanced Microwave Sounding Unit, and Microwave Humidity Sensor, as well as for the Defense Meteorological Satellite Program (DMSP)-F16 and DMSP-F18 Special Sensor Microwave Imager/Sounder microwave satellite sensors. For the validation of the MiRS rainfall rate technique, extensive comparisons with state-of-the-art precipitation products derived from rain gauge, ground-based radar, and satellite-based microwave observations are presented for different regions and seasons, and over land and ocean. The MiRS rainfall rate technique is shown to estimate precipitation, with a skill comparable to other satellite-based microwave precipitation algorithms, including the MSPPS, 3B40RT, and MWCOMB, while showing no discontinuities at coasts. This is a relevant result, considering that the MiRS is a system not merely designed to retrieve the rainfall rate but to consistently estimate a comprehensive set of atmospheric and surface parameters from microwave measurements.

The 12 papers in this special issue focus on the remote sensing and modeling of surface properties.

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

Satellite measurements from microwave instruments have made a significant contribution to the skill of numerical weather forecasting, on both global and regional scales. A FAST microwave Emissivity Model (FASTEM), which was developed by the Met Office, U.K., has been widely utilized to compute the surface emitted radiation in forward calculations. However, the FASTEM model was developed for frequencies in the range of 20-60 GHz, and it is biased at higher and lower frequencies. Several critical components such as variable sea surface salinity and full Stokes vector have not been generally taken into account. In this paper, the effects of the permittivity models are investigated, and a new permittivity model is generated by using the measurements for fresh and salt water at frequencies between 1.4 and 410 GHz. A modified sea surface roughness model from Durden and Vesecky is applied to the detailed two-scale surface emissivity calculations. This ocean emissivity model at microwave is now being used in the Community Radiative Transfer Model, and it has resulted in some major improvements in microwave radiance simulations. This paper is a joint effort of the Met Office, U.K., and the Joint Center for the Satellite Data Assimilation, U.S. The model is called as FASTEM-4 in the Radiative Transfer for TIROS Operational Vertical Sounder model.

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

The retrieval of land surface emissivity from satellite passive microwave measurements often requires the knowledge of various radiative components (e.g., atmospheric upwelling and downwelling radiation) contributed to the measurements. Under a cloud-free condition, atmospheric and surface radiative components can be derived from atmospheric temperature and water vapor, and surface temperature data. Thus, the quality of these auxiliary data sets directly affects the emissivity accuracy. From an emission-based radiative transfer equation, a set of relationships is derived to study the sensitivity of surface emissivity to the errors of brightness temperature, atmospheric transmittance, and surface temperature. As an example, the uncertainties in the Advanced Microwave Scanning Radiometer-Earth Observing System emissivity at 23 and 89 GHz may be much larger than the uncertainties of emissivity at lower frequencies due to the higher uncertainties in computing the water vapor absorption. The error in the land surface temperature is a main source of error in emissivity at the frequencies less than 19 GHz.

The Microwave Radiation Imager (MWRI) on board the FengYun-3A/B satellites observes the Earth atmosphere at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz with each having dual polarization. Its calibration system is uniquely designed with a main reflector viewing both cold and hot calibration targets. Two quasi-optical reflectors are used to reflect the radiation from the hot load and cold space to the main reflector. In the MWRI calibration process, a radiation loss in the beam transmission path must be taken into account. The loss factor in the hot load transmission path is derived using the antenna pattern data measured on ground and satellite data observing over the Amazon forest where the scene temperature is steady and close to the hot load. The instrument nonlinearity factors at different channels are also evaluated over a wide range of brightness temperatures and compared with the results from the ground vacuum test. After a cross-calibration with Windsat data, atmospheric products are derived from MWRI brightness temperatures with the accuracy similar to those from the legacy sensors (e.g., the Special Sensor Microwave/Imager).

The Community Radiative Transfer Model (CRTM) has been implemented in the Gridpoint Statistical Interpolation (GSI) system at the National Center for Environmental Prediction (NCEP) to simulate microwave and infrared radiances. We investigate impacts on analysis from a recent sensor, Special Sensor Microwave Imager Sounder (SSMIS). Both retrievals and radiance assimilation using the SSMIS show that SSMIS is unique in precisely determining stratospheric temperatures. We also utilize the SSMIS radiance for studying Hurricane Katrina. A "control-run" using current NCEP analysis field and a "test-run" using additional SSMIS data are compared. It shows again that the SSMIS radiance assimilation is helpful for hurricane forecasting.

The community radiative transfer model (CRTM), developed at U. S. Joint center for satellite data assimilation (JCSDA), has been used for infrared and microwave satellite radiance simulations and their derivatives to the surface/atmospheric parameters in the physical retrieval, data assimilation, and many others. CRTM has been become a key component in U. S. data assimilation for weather forecasting at the national center for environmental prediction (NCEP). This paper presents a new extension of the CRTM to ultraviolet (UV) and visible radiation for ozone and aerosol applications. This paper also demonstrates scattering effects on microwave radiative transfer.

Emissivity spectra computed by the FASTEM-3 model using global positioning system dropsonde wind as input are compared to emissivity retrieved from stringently coincident WINDSAT measurements, using a variational approach. This is done in both clear and rainy conditions to assess the validity of both the emissivity model and the retrieval technique in all conditions. Results of this comparison are presented for vertical and horizontal polarizations, in moderate to high wind conditions. In particular, a slope difference is found, and its potential sources are discussed in this paper.

The emission and scattering from desert surfaces are analyzed using simulations and measurements from the Special Sensor Microwave/Imager (SSM/I) and the Advanced Microwave Sounding Unit (AMSU) microwave satellite instruments. Deserts are virtually free of vegetation, so the satellite radiometers are able to observe the emissivities of different minerals, such as limestone and quartz. Moreover, since deserts contain little moisture, the thermal emission originates below the surface at a depth of many wavelengths. At high frequencies, where the penetration depth of radiation is smallest, the radiometric measurements display the large diurnal variation in surface temperature, which reaches its maximum at around 1 P.M. Conversely, at low frequencies, where the penetration depth is largest, the radiation measurements display the small diurnal variation of subsurface temperature, which reaches a minimum at around 6 A.M. In addition to these emission signals, sand particles also scatter microwave radiation. Volume scattering causes the measurements to decrease as the frequency increases; although compared to other scattering media (snow cover and precipitation), the larger absorption and fractional volume (i.e., solidity) of sand reduce the scattering. Although the scattering effect is small, SSM/I measurements between 19 and 85 GHz show that deserts scatter the upwelling microwave radiation in a manner similar to light precipitation, which makes it difficult to uniquely identify precipitation over arid regions. Interestingly, the higher frequency AMSU measurement at 150 GHz is nearly the same as at 89 GHz for deserts, whereas the 150-GHz measurement is much lower than at 89 GHz for precipitation. These different spectral features at high frequencies can provide a means of separating the scattering from desert surfaces from that of precipitation.

The 14 papers in this special issue focus on remote sensing and modeling of surface properties. The issue is devoted to the modeling and retrieval of surface parameters from satellite measurements, and the techniques used to assimilate surface-sensitive channels in numerical prediction models (NWPs).

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

The Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program F-16 satellite measures the Earth-emitted radiation at frequencies from 19 to 183 GHz. Compared with the Special Sensor Microwave/Imager (SSM/I), SSMIS has similar imaging channels except for two at 85.5 GHz replaced by the 91.655-GHz frequency. After the Naval Research Laboratory calibration of SSMIS imager channels, the temperature data record can be utilized operationally to derive both atmospheric and surface parameters. In this paper, several products are developed from the SSM/I heritage algorithms, including total precipitable water (TPW), cloud liquid water path (LWP), snow cover, sea ice cover, rain rate, and land surface temperature (LST). Some new products are also derived from the SSMIS, such as land emissivity. The retrieved products from F-15 SSM/I and F-16 SSMIS are intercompared to quantify the mean bias and standard deviation. It is found that because of both the relatively small mean bias and standard deviation, the F-16 SSMIS products, such as TPW, cloud LWP, snow, and sea ice, may replace the SSM/I products for operational use. However, discrepancies remain in the global rainfall estimates, LST, and land emissivity produced by each sensor. This is likely due to the imperfect F-16 SSM/I-like channels to F-15 SSM/I channels' linear mapping, particularly for 91.655-GHz channels, whose frequency is shifted from 85.5 GHz in SSM/I.

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

The ability to provide temperature and water-vapor soundings under extreme weather conditions, such as hurricanes, could extend the coverage of space-based measurements to critical areas and provide information that could enhance outcomes of numerical weather prediction (NWP) models and other storm-track forecasting models, which, in turn, could have vital societal benefits. An NWP-independent 1D-VAR system has been developed to carry out the simultaneous restitutions of atmospheric constituents and surface parameters in all weather conditions. This consistent treatment of all components that have an impact on the measurements allows an optimal information-content extraction. This study focuses on the data from the NOAA-18 satellite (AMSUA and MHS sounders). The retrieval of the precipitating and nonprecipitating cloud parameters is done in a profile form, taking advantage of the natural correlations that do exist between the different parameters and across the vertical layers. Stability and the problem's ill-posed nature are the two classical issues facing this type of retrieval. The use of empirically orthogonal-function decomposition leads to a dramatic stabilization of the problem. The main goal of this inversion system is to be able to retrieve independently, with a high-enough accuracy and under all conditions, the temperature and water-vapor profiles, which are still the two main prognostic variables in numerical weather forecast models. Validation of these parameters in different conditions is undertaken in this paper by comparing the case-by-case retrievals with GPS-dropsondes data and NWP analyses in and around a hurricane. High temporal and spatial variabilities of the atmosphere are shown to present a challenge to any attempt to validate the microwave remote-sensing retrievals in meteorologically active areas.

There is an increasing demand for intercalibrating the polar-orbiting radiometers on different satellites to achieve the consistency and traceability required for long-term climate studies with the 25+ years of NOAA satellite data. Also, the calibration of current operational radiometers needs to be linked to those of the next generation NPOESS (National Polar-orbiting Operational Environmental Satellite System) satellites. The simultaneous nadir overpasses (SNOs)/simultaneous conical overpass (SCO) method developed at NOAA/NESDIS has been applied to the intersatellite calibration of radiometers in the infrared, visible/near-infrared, and microwave with excellent results. In this paper, the SNO/SCO methodology is introduced. Preliminary intersatellite calibration results for AVHRR, MODIS, SSM/I, AIRS, HIRS, and AMSU on operational and historical satellites are presented. The plans for establishing the calibration links between POES (Polar Operational Environmental Satellites) and NPOESS radiometers using the SNO/SCO method are discussed.

A new methodology is developed to monitor and calibrate the Windsat 3rd and 4th Stokes parameters through tropical rainforest measurements over Amazon and the central Africa. It is found that the Windsat 4th Stokes parameter at 18 GHz has biases on an order of 0.5 Kelvin, which could severely impact the wind vector retrievals.

With the launch of the NOAA-15 satellite in May 1998, a new generation of passive microwave sounders was initiated. The Advanced Microwave Sounding Unit (AMSU), with 20 channels spanning the frequency range from 23-183 GHz, offers enhanced temperature and moisture sounding capability well beyond its predecessor, the Microwave Sounding Unit (MSU). In addition, by utilizing a number of window channels on the AMSU, the National Oceanic and Atmospheric Administration (NOAA) expanded the capability of the AMSU beyond this original purpose and developed a new suite of products that are generated through the Microwave Surface and Precipitation Products System (MSPPS). This includes precipitation rate, total precipitable water, land surface emissivity, and snow cover. Details on the current status of the retrieval algorithms (as of September 2004) are presented. These products are complimentary to similar products obtained from the Defense Meteorological Satellite Program Special Sensor Microwave/Imager (SSMI) and the Earth Observing Aqua Advanced Microwave Scanning Radiometer (AMSR-E). Due to the close orbital equatorial crossing time between NOAA-16 and the Aqua satellites, comparisons between several of the MSPPS products are made with AMSR-E. Finally, several application examples are presented that demonstrate their importance to weather forecasting and analysis, and climate monitoring.

The measurements from satellite microwave imaging and sounding channels are simultaneously utilized through a one-dimensional (1-D) variation method (1D-var) to retrieve the profiles of atmospheric temperature, water vapor and cloud water. Since the radiative transfer model in this 1D-var procedure includes scattering and emission from the earth's atmosphere, the retrieval can perform well under all weather conditions. The iterative procedure is optimized to minimize computational demands and to achieve better accuracy. At first, the profiles of temperature, water vapor, and cloud liquid water are derived using only the AMSU-A measurements at frequencies less than 60 GHz. The second step is to retrieve rain and ice water using the AMSU-B measurements at 89 and 150 GHz. Finally, all AMSU-A/B sounding channels at 50-60 and 183 GHz are utilized to further refine the profiles of temperature and water vapor while the profiles of cloud, rain, and ice water contents are constrained to those previously derived. It is shown that the radiative transfer model including multiple scattering from clouds and precipitation can significantly improve the accuracy for retrieving temperature, moisture and cloud water. In hurricane conditions, an emission-based radiative transfer model tends to produce unrealistic temperature anomalies throughout the atmosphere. With a scattering-based radiative transfer model, the derived temperature profiles agree well with those observed from aircraft dropsondes.