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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.

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.

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.

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 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.

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 $0^\circ\!\sim\! 60^\circ$ 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.

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' Humidite Intropicale par Radiometrie (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.

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.

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 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 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.

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.

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.

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 CO2 sounding 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.

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 MWTS O - BDF biases found in our earlier work is mostly induced by these frequency shifts, where O represents MWTS observations and BDF is model simulations. The mean difference of brightness temperature simulations with (BSF) and without (BSF) incorporating the frequency shifts into the radiative transfer model resembles the O - BSF biases. 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 MWTS O - Bshifted biases become constant as those of MetOp-A/NOAA-18 AMSU-A data. For climate applications, the FY-3A MWTS brightness temperatures adjusted by subtracting BSF - BDF match quite well with the MetOp-A/NOAA-18 AMSU-A data at the simultaneous nadir overpass locations in both the Arctic and Antarctic.

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.

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 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.

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.

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.

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 Meteo-France. Moreover, both the emissivity library and the Meteo-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 Meteo-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.

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.

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.

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.

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 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 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.

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.

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

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).