The occurrence of atmospheric blocking signifies a remarkable transition from zonal to meridional circulation at 500 hPa. Its cut-off low may evolve into a Northeast China Cold Vortex (NCCV), causing extreme weather such as heavy rainfall and hailstorms. In this study, satellite-based microwave temperature-sounding data are used for tracking NCCV movements and for detecting blocking occurrences. The NCCV movement tracks are identified by the warm centers of brightness temperature measurements of the sounding channel whose peak weighting function is located at 200 hPa. For the detection of blocking, a linear regression is firstly established between 500-hPa geopotential height and AMSU-A channels 4-8; and the blocking index is then calculated for the identification of blocking. Here, the block index is calculated as the difference in 500-hPa geopotential height between 40° and 60°N. Time-longitude plots of the blocking index, together with sequences of 500-hPa height charts, for two blocking events that occurred in May 2023 and August 2020, compared well with those obtained by the ERA5 global reanalysis. The cut-off low of both blockings developed into NCCVs. This enables operational forecasters to directly and conveniently extract geopotential height information from satellite raw measurements of brightness temperature for weather diagnosis and forecasting. It also provides novel methodologies for analyzing of atmospheric large-scale circulation in recent decades, given more than 26 years of satellite-based microwave temperaturesounding data globally.

The center positions and sizes of tropical cyclones (TCs) are very important for monitoring and forecasting their track and intensity changes. Brightness temperature (TB) observations of geostationary satellite imagers have high temporal and spatial resolutions, and can thus reflect cloud and rain structures within and around TCs. In this paper, an objective method is developed to determine the center positions and eye and eyewall radii based on Advanced Himawari Imager (AHI) TB observations at channel 13 (10.45 μm) from the Japanese geostationary satellite Himawari-8. Results show that the positioning deviation of AHI-determined centers of Typhoon Trami from the best track from the Joint Typhoon Warning Center (JTWC) is smaller when TC intensity is stronger. The average deviation of the center positions is 28.18 km. The accuracy of the proposed method is validated by the results of ten other typhoons in the Northwest Pacific during August and September 2018. The AHI-determined radii of both eye and eyewall match well with the characteristic distributions of clouds and rainbands near the center of Trami. During the rapid intensification of Trami, Trami centers are characterized by low-values TBs that have an elliptical shape rotating counterclockwise with time. This may reveal a possible existence of vortex Rossby waves with azimuthal wavenumber 2. The TC center position and eye and eyewall radius parameters obtained from the TB observations of the geostationary satellite imager could be applied to an advanced vortex initialization to improve TC track and intensity forecasts.

Hurricane top structures are not well captured by airborne or dropsonde observations. Total column ozone (TCO) observations provided by the Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper (NM) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite are employed in an investigation of hurricane top structures. We show that the OMPS NM TCO data can capture the top structures of Hurricane Maria (2017) over the Atlantic Ocean. An observed local maximum of TCO in the eye region reveals a strong upper tropospheric downward motion that lowers the tropopause above the hurricane eye. A rainband-like distribution of low TCO content reflects strong convection areas where the tropopause is raised and well correlates spatially with the high cloud top regions derived from the S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS). A sixth-order even polynomial fitting is used to reveal the TCO radial structures by introducing two characteristic parameters. One is a radial distance parameter ($R_{\mathrm {TCO}}$) representing the spatial range, and the other is an intensity parameter defined as the TCO difference from the hurricane center to 600-km radial distance from the hurricane center ($\Delta {\mathrm {TCO}}_{600\,{\mathrm {km}}} $). Based on an analysis of ten hurricanes over the Northern Atlantic Ocean in 2017, we show that before a hurricane reaches its maximum strength, there is always a decrease of$R_{\mathrm {TCO}}$and an increase of$\Delta {\mathrm {TCO}}_{600\,{\mathrm {km}}} $. It is anticipated that more accurate initial hurricanes could be produced if the TCO structures were combined with other surface, near surface, and tropospheric information in vortex initialization.

Developed here is an algorithm for determining the infrared (IR) cloud-top phase for advanced Himawari imager (AHI) measurements from the Japanese geostationary satellite Himawari-8. The tests and decision tree used in the AHI algorithm are different from those in the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 cloud product algorithm. Verification of AHI cloud-top phase results with the Cloud-Aerosol Lidar with orthogonal polarization product over a four-month period from March to June of 2017 over the North Pacific gives hit rates of 80.20% (66.33%) and 86.51% (80.61%) for liquid-water and randomly oriented-ice cloud tops, respectively, if clear-sky pixels are excluded (included) from the statistics. Also made are intercomparisons between AHI and MODIS IR cloud-top phase products over the North Pacific in June 2017. AHI liquid-water-phase determinations agree with MODIS liquid-water-phase determinations at an agreement rate of 83.68%, showing a dependence on MODIS zenith angles. The agreement rate of ice phase classifications between AHI and MODIS is 93.54%. The MODIS IR product contains some unreasonable ice-phase pixels over oceans, as well as uncertain-phase pixels over land, and has limitations for daytime liquid-water-phase identifications over the Indo-China Peninsula. Limitations of the AHI cloud-top phase algorithm are mainly caused by difficulties in identifying liquid-water-phase clouds over sun-glint regions and during twilight.

Cloud optical/microphysical properties (COMPs) with a high spatiotemporal resolution are required for monitoring the evolution of convective clouds, studying aerosol–cloud–precipitation interactions, and evaluating cloud microphysics parameterization schemes in weather and climate models. This study applied the bispectral method, commonly used for COMP retrievals, to Advanced Himawari Imager (AHI)/Himawari-8 observations. The AHI daytime cloud optical thickness ($\tau$) and cloud-top particle effective radius (Re) retrievals were then evaluated with the Collection-6.1 Moderate Resolution Imaging Spectroradiometer cloud product (MOD06) for a three-month period from June 1, 2017, to August 31, 2017. Overall, the AHI-retrieved COMP results are in good agreement with the MOD06 product. The correlation coefficients of$\tau $andRebetween the AHI retrievals and MOD06 products are 0.76–0.89 and 0.63–0.87, respectively. Overestimations are mainly observed in the 1.6-$\mu \text{m}$Re(Re1.6) and ice-phase 3.9-$\mu \text{m}$Re(Re3.9) retrievals. There are$\tau $dependencies in the retrieval biases of$\tau $and Re1.6, as well as angular dependencies in the retrieval biases of$\tau $and ice-phase Re3.9. Moreover, the monthly mean composites for three COMP retrievals from the MOD06 and AHI algorithms are compared. Significant differences are only found between the two Re1.6products. This study also finds that AHI-retrievedReand$\tau $values have a diurnal variation over land but not over oceans.

The conical-scanning Tropical Rainfall Measuring Mission Microwave Imager (TMI), which plays an important role in monitoring global precipitation, has nine channels. A clear striping noise feature is found in pitch-over maneuver data of deep-space and earth-view observations at all nine channels of the TMI. A modified striping noise mitigation algorithm is used to extract the striping noise from deep-space and earth-scene observations. The magnitude of the striping noise extracted from TMI channel 10.65 V, 10.65 H, and 37.0 H measurements is about 0.4 K and more than 1.5 K for channel 85.5 V and 85.5 H measurements. After striping noise mitigation, no visible striping feature is seen in the observation-minus-simulation distributions of both deep-space and earth-scene brightness temperatures. Comparisons of the liquid water path (LWP) retrieved from TMI brightness temperatures with and without striping noise mitigation show that the striping noise in brightness temperatures results in a striping feature in LWP retrievals. When the striping noise is not removed from TMI observations, LWP retrievals exhibit a cross-track striping feature. The striping noise leads to a greater retrieval difference in larger LWP areas.

Observations from the microwave sounding unit (MSU), from 1978 to 2006 and its successor, the Advanced Microwave Sounding Unit-A (AMSU-A, 1998-present), and onboard the National Oceanic and Atmospheric Administration's polar-orbiting satellites have been widely used for estimating global climate trends. The MSU has a long-term data set, but it is difficult to obtain cloud information like the cloud liquid water path (LWP) directly from this data set. To monitor and investigate the cloud effect on global upper air temperature trends using MSU observations, a cloud detection algorithm must be developed for the MSU. Considering the similar center frequencies of MSU channel 1 and AMSU-A channel 3, a new cloud detection algorithm is established based on the differences between observed and model-simulated brightness temperatures (O-B-μ(α)-μ(φ)) of MSU channel 1 or AMSU-A channel 3 over oceans, where μ(φ) is a latitudinal dependent global mean bias and μ(α) is the global mean bias depending on scan angle. If a data point satisfies the condition of O-B-μ(α)-μ(φ) ≥ 1 K, it is removed from the clear sky data set. In order to ensure that those points that are partially affected by the clouds, such as clear and cloud mixed fields-of-views located near cloud edges and/or within optically thin clouds, all data points within the 60-km radial distance of the detected point are removed. Validated with the AMSU-A derived LWP retrievals, about 50% of the clear sky data are successively identified and 99% of the cloudy radiances are successively removed for all NOAA-15 AMSU-A data on January 15, April 15, July 15, and October 15, 2002.

Advanced Baseline Imager (ABI) aboard GOES-16 geostationary satellite represents the new generation of GOES imager instruments. It can provide more channels, higher spatial resolution and observation frequency than any previously launched GOES imagers. In this study, impacts of assimilating ABI radiance data at different model horizontal resolutions on quantitative precipitation forecast (QPF) skill over the continental U.S. are examined. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical interpolation (GSI) analysis system and Advanced Weather Research and Forecast model (WRF-ARW) are employed. After removing cloudy and precipitation-affected brightness temperatures through using an infrared (IR) cloud algorithm and implementing a bias correction, positive impacts are obtained for ABI data assimilation to improve QPFs associated with a typical spring precipitation case. Higher resolution experiments (6 km) improve the QPFs more significantly for heavier rainfall than coarser resolution experiments (15 km).

The Joint Polar Satellite System (JPSS)-1 satellite, also known as National Oceanic and Atmospheric Administration (NOAA)-20, was successfully launched onto a sun-synchronous orbit on November 18, 2017. One of the instruments carried on NOAA-20 is the Advanced Technology Microwave Sounder (ATMS). The first set of ATMS observation data was sent back to Earth on November 29, 2017. Using the first month of the observed data, the instrument biases are estimated based on the differences between observed and simulated antenna temperatures. Model simulations are generated by using the Global Positioning System (GPS) radio occultation (RO) temperature and water vapor profiles from COSMIC, KOMPSAT, MetOp-A/-B GRAS as input to the Community Radiative Transfer Model (CRTM). The same procedure is also carried out to ATMS observations from the Suomi National Polar-orbiting Partnership (S-NPP) satellite during the same one-month time period. It is shown that ATMS channels 7–9, for which the GPS RO data are most accurate, from NOAA-20 are more negatively biased than the corresponding channels of S-NPP. Further investigations will be carried out as more NOAA-20 observations become available.

The S-NPP ATMS channels have across-track striping noise, which can see explicitly in all channels from pitch maneuver observations and upper air temperature-sounding channels by the NWP O-B fields. Such a striping noise is also seen in NWP O-B fields of upper air temperature sounding channels from ATMS onboard NOAA-20. The striping noise mitigation algorithm that was developed for S-NPP is applied to ATMS channels from NOAA-20. It is shown that the NOAA-20 ATMS also have an across-track striping noise feature that can see in the NWP O-B fields of upper air temperature-sounding channels. However, the ATMS striping noise from NOAA-20 is less significant in magnitudes than that of S-NPP. Although of small magnitude (less than 0.3 K), the striping noise appears in the ATMS hurricane warm core retrievals in upper troposphere around 250 hPa with a much larger ampllitude (~0.7 K).

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.

The Advanced Himawari Imager (AHI) onboard Japanese geostationary satellite Himawari-8 provides two more visible, three more near-infrared, and six more infrared channels than the only one visible and four infrared channels available from the previous geostationary imager instruments. By taking advantage of AHI's newly added channels 1, 3, and 4 with wavelengths centered at 0.46, 0.64, and $0.86 μm, respectively, a fast cloud detection algorithm is developed. Since the spectral differences of the reflectance between any two of AHI's channels 1, 3, and 4 over clouds are smaller than those over land and ocean, a visible-based cloud index (VCI) for daytime cloud detection can thus be defined by the root mean square of the three differences between any two of these three channels. An AHI pixel is identified as cloudy if the VCI is smaller than a threshold, which has different values over ocean and land. Cloud detection is further adjusted by a bias correction using AHI channels 7 and 13. The average accuracy of the proposed simple cloud detection is comparable with those obtained from a more complicated cloud mask algorithm involving not only more channels but also model simulations. It is also found that the bias correction is needed mostly over cirrus clouds and Gobi.

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 Advanced Microwave Scanning Radiometer 2 (AMSR2) measurements at K-band channels are used for snow retrieval. However, television (TV) signals transmitted from DirecTV satellites at the K-band, if reflected by snow surfaces, could enter the antenna of AMSR2 to introduce errors in AMSR2 snow products. This letter investigates TV frequency interference (TFI) in AMSR2 K-band measurements over areas of land covered with snow. Since a necessary condition for TFI over land to occur is for AMSR2 measurements to have small glint angles, a principal component analysis algorithm constrained by TFI glint angles for TFI detection is thus developed in this letter. Using AMSR2 observations in January 2014, it is shown that TFI signals exist along the two 55° incident angle curves of DirecTV-11 and DirecTV-12, which are located around 44° N over the North American continent.

Microwave Humidity Sounder (MWHS) onboard the Chinese FengYun-3C satellite has a total of 12 channels. Eight of these channels are located near the 118-GHz oxygen absorption band for probing atmospheric temperature and humidity fields from space. While the water vapor sounding channels near 183 GHz have been extensively studied in the past, we report the first satellite observations at 118 GHz. In this paper, the MWHS calibration accuracy is assessed by comparing the satellite observations with simulations. Using the collocated Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) data in clear-sky conditions as inputs to Community Radiative Transfer Model, MWHS brightness temperatures are simulated for the five upper level sounding channels 2-9 located near the 118-GHz oxygen absorption band. For quality control of clear-sky radiance, a new cloud index is first developed based on the two MWHS window channels 1 and 10. Monthly mean biases of the antenna brightness temperature observations for the 118-GHz sounding channels are quantified by using more than 2000-5000 collocated COSMIC and MWHS data from August 2014 to February 2015. It is found that the bias of MWHS data relative to COSMIC RO simulation is dependent on channel and ranges within ±1.5 K. The standard deviation from the bias is the largest at MWHS channel 2, indicating a larger variability of the measurements of channel 2 than the other channels.

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.

Television (TV) radio frequency interference (TFI) signals are found in the Advanced Microwave Scanning Radiometer 2 (AMSR2) observations of those channels with their frequencies centered at 18.7- or 10.65-GHz frequencies over coastal regions near the U.S. and Europe, respectively. When TV signals are reflected off the ocean surface and get into AMSR2 field of views, the AMSR2-measured radiance contains not only information of natural emission from Earth's surface but also the reflected TV signals. If not detected and corrected, TFI introduces errors into the geophysical retrieval products. The occurrence and intensity of TFI are determined by the angle between the observation beam vector and the reflected TV signal vector (i.e., TFI glint angle) and the background TV signal intensity. In this paper, an empirical model is developed to quantitatively calculate the contribution of TFI signals to AMSR2 observations based on TFI glint angle and TV signal intensity. This empirical model is then applied to AMSR2 K-band channels over North America and X-band channels over Europe. It is shown that the annual mean bias for the TFI-affected observations of the 18.7-GHz channel at horizontal (vertical) polarization reduces from a value of more than 5 K (2 K) to about -0.5 K (0.5) after TFI correction over the coastal ocean near North America. The annual mean bias for the TFI-affected observations of the 10.65-GHz channel at horizontal (vertical) polarization reduces from a value of about 2.5 K to about -0.7 K (0.5 K) after TFI correction over the coastal ocean near Europe. False maxima in AMSR2-retrieved cloud liquid water path and dry anomalies in AMSR2-retrieved total precipitable water near the coastal regions are also eliminated after incorporating the TFI correction.

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.

In this paper, the Backus-Gilbert (B-G) method was used for the conversion from Advanced Technology Microwave Sounder (ATMS) FOVs to AMSU-A FOVs. This method provides not only an optimal combination of measurements within a specified region but also a quantitative measure of the tradeoff between resolution and noise. Based on a subpixel microwave antenna temperature simulation technique, ATMS observations at a specified FOV size with 1.1° sampling interval are simulated. Errors of remapping results were quantified by using simulated data sets and real AMSU observations. It is shown that the biases and/or standard deviations of brightness temperatures are significantly reduced by using the B-G generated remapping coefficients. For K/Ka bands, a resolution enhancement by the remap of ATMS observations introduces about 0.6 K increase in noise. For other bands, the channel sensitivity was improved for the remapped data.

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 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.

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.

In the above-named article [ibid., vol. 50, no. 12, pp. 4892-4902, Dec. 2012], Fig. 6 is incorrect. The correct one is printed here.

Following the successful launch of the first polar-orbiting morning-configured satellite, FY-3A, on May 27, 2008, in a new Fengyun three (FY-3) series, the second afternoon-configured polar-orbiting satellite (FY-3B) was launched on November 5, 2010. The four-channel Microwave Temperature Sounder (MWTS) was onboard both FY-3A/B satellites, with designed channel frequency similar to channels 3, 5, 7, and 9 of the Advanced Microwave Sounding Unit-A (AMSU-A). This study assesses the quality of the brightness temperature measurements from FY-3B MWTS by comparing them with numerical weather prediction (NWP) model simulations and NOAA-18 AMSU-A measurements with the same frequencies. A strong latitudinal-dependent bias is found for both MWTS channel 3 and AMSU-A channel 7. At channel 4, the brightness temperatures are contaminated within a small latitudinal zone ( ~ 30°-40°N) of the Northern Hemisphere. It is also found that the MWTS channel 4 bias is strongly asymmetric across the scan and the fourth field of view (FOV4) of channel 4 is globally and systematically warmer than its neighboring FOVs. These anomalous biases may arise from sidelobe effect and interferences of the signal transmitted from some unknown sources. A quality control algorithm is developed to remove the anomalous data at MWTS channel 4 for its applications in NWP.

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.

Microwave Radiation Imagers (MWRIs) onboard the FengYun (FY)-3A/B satellites of China Meteorological Administration were launched on May 28, 2008, and November 5, 2010, respectively. They both observe the Earth atmosphere and land surface at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz with dual polarization. After extensive on-orbit calibrations, the MWRI Level-1 data were collocated in space and time with the data from Aqua Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), F18 Special Sensor Microwave Imager Sounder (SSMIS), and Tropical Rainfall Measuring Mission Microwave Imager for cross-calibration. A forward radiative transfer model was used to simulate the clear sky brightness temperatures at the MWRI frequencies over ocean. The differences between MWRI observations and model simulations, referred to as “O-A,” and the double difference results from pairs of MWRI and AMSR-E were examined. Comparing to the biases between AMSR-E/SSMIS measurements and model simulations, the biases for MWRI are small and stable. Atmospheric and surface geophysical parameters are retrieved from MWRI observations using the heritage algorithms. It is shown that these environmental data records from MWRI are comparable with those similar data products from AMSR-E and SSMIS. Their biases from each other seemed to be minimal.

MicroWave Humidity Sounder (MWHS) onboard both Fengyun-3A (FY-3A) and FY-3B satellites have three channels (channels 3-5) near the 183-GHz water-vapor absorption line. These channel frequencies are also used in other instruments such as Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounder (MHS) onboard MetOp and NOAA satellites. Both MWHS and MHS are cross-track scanners. In this paper, a comparison between the simulated brightness temperatures with MWHS measurements clearly shows that MWHS observations from the three sounding channels contain a scan-angle-dependent cohesive noise along the instrument scanline. This noise does not cancel out when a large amount of data over a sufficiently long period of time is averaged, which eliminates the possibility of such a noise to arise from the natural variability of the atmosphere and the surface. The noises are around 0.3, 0.2, and 0.2 K for channels 3-5, respectively. A principle component analysis is used for the characterization of this cohesive noise using one-month FY-3B MWHS data. It is shown that the MWHS cohesive noise is primarily contained in the first principal component (PC) mode, which mainly describes a scan-angle-dependent brightness temperature variation, i.e., a unique feature of the cross-tracking instrument. The first PC accounts for more than 99.91 % total variance in the three MWHS sounding channels. A five-point smoother is then applied to the first PC, which effectively removes such a data noise in the MWHS data. The reconstruction of the MWHS radiance spectra using the noise-filtered first PC component is of good quality. The scan-angle-dependent bias from the reconstructed MWHS data becomes more uniform and is consistent with the NOAA-18 MHS data.

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.