Microwave Radiation Imager Research Articles

Retrieval of Snow Depth on Sea Ice in the Arctic Using the FengYun-3B Microwave Radiation Imager

Snow on sea ice is a sensitive indicator of climate change because it plays an important role regulating surface and near surface air temperatures. Given its high albedo and low thermal conductivity, snow cover is considered a key reason for amplified warming in polar regions. This study focuses on retrieving snow depth on sea ice from brightness temperatures recorded by the Microwave Radiation Imager (MWRI) on board the FengYun (FY)-3B satellite. After cross calibration with the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) Level 2A data from January 1 to May 31, 2011, MWRI brightness temperatures were used to calculate sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm. Snow depths were derived according to the proportional relationship between snow depth and surface scattering at 18.7 and 36.5 GHz. To eliminate the influence of uncertainties in snow grain sizes and sporadic weather effects, seven-day averaged snow depths were calculated. These results were compared with snow depths from two external data sets, the IceBridge ICDIS4 and AMSR-E Level 3 Sea Ice products. The bias and standard deviation of the differences between the MWRI snow depth and IceBridge data were respectively 1.6 and 3.2 cm for a total of 52 comparisons. Differences between MWRI snow depths and AMSR-E Level 3 products showed biases ranging between −1.01 and −0.58 cm, standard deviations from 3.63 to 4.23 cm, and correlation coefficients from 0.61 to 0.79 for the different months.

Tropical Cyclone Rain Retrievals from FY-3B MWRI Brightness Temperatures Using the Goddard Profiling Algorithm (GPROF)

AbstractThis study focuses on the tropical cyclone rainfall retrieval using FY-3B Microwave Radiation Imager (MWRI) brightness temperatures (Tbs). The GPROF, a fully parametric approach based on the Bayesian scheme, is adapted for use by the MWRI sensor. The MWRI GPROF algorithm is an ocean-only scheme used to estimate rain rates and hydrometeor vertical profiles. An a priori database is constructed from MWRI simulated Tbs, the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) combined data, and ancillary data resulting in about 100 000 rainfall profiles. The performance of MWRI retrievals is consistent with DPR observations, even though MWRI retrievals slightly overestimate low rain rates and underestimate high rain rates. The total bias of MWRI retrievals is less than 13% of the mean rain rate of DPR precipitation. Statistical comparisons over GMI GPROF, GMI Hurricane GPROF (HGPROF), and MWRI GPROF retrievals show MWRI GPROF retrievals are consistent in terms of spatial distribution and rain estimates for TCs compared with the other two estimates. In terms of the global precipitation, the mean rain rates at different distances from best track locations for five TC categories are used to identify substantial differences between mean MWRI and GMI GPROF retrievals. After correcting the biases between MWRI and GMI retrievals, the performance of MWRI retrievals shows slight overestimate for light rain rates while underestimating rain rates near the eyewall for category 4 and 5 only.

Spatial Resolution Enhancement of Satellite Microwave Radiometer Data with Deep Residual Convolutional Neural Network

Satellite microwave radiometer data is affected by many degradation factors during the imaging process, such as the sampling interval, antenna pattern and scan mode, etc., leading to spatial resolution reduction. In this paper, a deep residual convolutional neural network (CNN) is proposed to solve these degradation problems by learning the end-to-end mapping between low-and high-resolution images. Unlike traditional methods that handle each degradation factor separately, our network jointly learns both the sampling interval limitation and the comprehensive degeneration factors, including the antenna pattern, receiver sensitivity and scan mode, during the training process. Moreover, due to the powerful mapping capability of the deep residual CNN, our method achieves better resolution enhancement results both quantitatively and qualitatively than the methods in literature. The microwave radiation imager (MWRI) data from the Fengyun-3C (FY-3C) satellite has been used to demonstrate the validity and the effectiveness of the method.

<inline-formula> <tex-math notation="LaTeX">$\ell_0$ </tex-math> </inline-formula> Sparse Approximation of Coastline Inflection Method on FY-3C MWRI Data

The microwave radiation imager (MWRI) located onboard the FengYun-3C (FY-3C) satellite provides a considerable amount of critical information for numerical weather predictions. Obtaining accurate geolocation results from the FY-3C MWRI data is of great importance. In this letter, we improve the traditional coastline inflection method (CIM) and propose an $\ell _{0}$ sparse approximation model for geolocation error estimation and correction. Specifically, we propose using the jump point of the step function to estimate the true coastline point. This approach can characterize the geolocation errors more accurately than the CIM, which further improves the geolocation accuracy. In the theoretical part, we provide a complete solution to obtain the step function through an iterative blind deconvolution. For a practical use, we demonstrate the effectiveness of the proposed method for geolocation error estimation through quantitative results obtained on the FY-3C MWRI data. The experimental results show that the proposed method can achieve an improvement of up to 33.33% in the standard deviation of geolocation errors (approximately 0.00030) compared to the traditional CIM (approximately 0.00045). Furthermore, we also apply the proposed method to the FY-3C satellite and improve the geolocation accuracy of the MWRI data through geolocation error correction.

Effect of radio-frequency interference on the retrieval of land surface temperature from microwave radiation imager

Effect of radio-frequency interference on the retrieval of land surface temperature from microwave radiation imager

Tropical Cyclone Rainfall Estimates from FY-3B MWRI Brightness Temperatures Using the WS Algorithm

A rainfall retrieval algorithm for tropical cyclones (TCs) using 18.7 and 36.5 GHz of vertically and horizontally polarized brightness temperatures (Tbs) from the Microwave Radiation Imager (MWRI) is presented. The beamfilling effect is corrected based on ratios of the retrieved liquid water absorption and theoretical Mie absorption coefficients at 18.7 and 36.5 GHz. To assess the performance of this algorithm, MWRI measurements are matched with the National Snow and Ice Data Center (NSIDC) precipitation for six TCs. The comparison between MWRI and NSIDC rain rates is relatively encouraging, with a mean bias of −0.14 mm/h and an overall root-mean-square error (RMSE) of 1.99 mm/h. A comparison of pixel-to-pixel retrievals shows that MWRI retrievals are constrained to reasonable levels for most rain categories, with a minimum error of −1.1% in the 10–15 mm/h category; however, with maximum errors around −22% at the lowest (0–0.5 mm/h) and highest rain rates (25–30 mm/h). Additionally, Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) Tbs are applied to retrieve rain rates to assess the sensitivity of this algorithm, with a mean bias and RMSE of 0.90 mm/h and 3.11 mm/h, respectively. For the case study of TC Maon (2011), MWRI retrievals underestimate rain rates over 6 mm/h and overestimate rain rates below 6 mm/h compared with Precipitation Radar (PR) observations on storm scales. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rainfall data provided by the Remote Sensing Systems (RSS) are applied to assess the representation of mesoscale structures in intense TCs, and they show good consistency with MWRI retrievals.

Assessment and Assimilation of FY-3 Humidity Sounders and Imager in the UK Met Office Global Model

China’s FengYun 3 (FY-3) polar orbiting satellites are set to become an important source of observational data for numerical weather prediction (NWP), atmospheric reanalyses, and climate monitoring studies over the next two decades. As part of the Climate Science for Service Partnership China (CSSP China) program, FY-3B Microwave Humidity Sounder 1 (MWHS-1) and FY-3C MWHS-2 observations have been thoroughly assessed and prepared for operational assimilation. This represents the first time observations from China’s polar orbiting satellites have been used in the UK’s global NWP model. Since 2016, continuous data quality monitoring has shown occasional bias changes found to be correlated to changes in the energy supply scheme regulating the platform heating system and other transient anomalies. Nonetheless, MWHS-1 and MWHS-2 significantly contribute to the 24-h forecast error reduction by 0.3% and 0.6%, respectively, and the combination of both instruments is shown to improve the fit to the model background of independent sounders by up to 1%. The observations from the Microwave Radiation Imager (MWRI) also are a potentially significant source of benefits for NWP models, but a solar-dependent bias observed in the instrument half-orbits has prevented their assimilation. This paper presents the bases of a correction scheme developed at the Met Office for the purpose of a future assimilation of MWRI data.

Evaluation of Multiple Satellite-Based Soil Moisture Products over Continental U.S. Based on In Situ Measurements

Measurements of soil water content are of great importance for climatic researches and hydrological applications. Satellite remote sensing techniques have provided convenient access to global soil moisture data at high temporal resolution. The evaluation of remotely sensed soil moisture products from multiple sensors are fundamental for understanding the quality and usefulness of the products and are helpful for the improvements of the retrieval algorithms. In this study, we evaluated four satellite-based soil moisture products against two in situ networks over continental U.S. These products are the Soil Moisture and Ocean Salinity (SMOS), the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E), the Fengyun-3B/Microwave Radiation Imager (FY-3B/MWRI), and the Essential Climate Variable (ECV) soil moisture products. The evaluation results show that the temporal characteristics of ECV and SMOS soil moisture products are consistent with those of in situ measurements. Larger errors and bias exist in AMSR-E and FY-3B/MWRI over the study region. In general, the ECV product performs the best comparing with other products. Therefore, the ECV product is recommended in regional hydrological applications and water resource management over the continental U.S. for its good performance in absolute values and soil moisture anomalies.

Feasibility Study on S-Band Microwave Radiation and 3D-Thermal Infrared Imaging Sensor-Aided Recognition of Polymer Materials from End-of-Life Vehicles.

With the increase the worldwide consumption of vehicles, end-of-life vehicles (ELVs) have kept rapidly increasing in the last two decades. Metallic parts and materials of ELVs can be easily reused and recycled, but the automobile shredder residues (ASRs), of which elastomer and plastic materials make up the vast majority, are difficult to recycle. ASRs are classified as hazardous materials in the main industrial countries, and are required to be materially recycled up to 85–95% by mass until 2020. However, there is neither sufficient theoretical nor practical experience for sorting ASR polymers. In this research, we provide a novel method by using S-Band microwave irradiation together with 3D scanning as well as infrared thermal imaging sensors for the recognition and sorting of typical plastics and elastomers from the ASR mixture. In this study, an industrial magnetron array with 2.45 GHz irradiation was utilized as the microwave source. Seven kinds of ELV polymer (PVC, ABS, PP, EPDM, NBR, CR, and SBR) crushed scrap residues were tested. After specific power microwave irradiation for a certain time, the tested polymer materials were heated up to different extents corresponding to their respective sensitivities to microwave irradiation. Due to the variations in polymer chemical structure and additive agents, polymers have different sensitivities to microwave radiation, which leads to differences in temperature rises. The differences of temperature increase were obtained by a thermal infrared sensor, and the position and geometrical features of the tested scraps were acquired by a 3D imaging sensor. With this information, the scrap material could be recognized and then sorted. The results showed that this method was effective when the tested polymer materials were heated up to more than 30 °C. For full recognition of the tested polymer scraps, the minimum temperature variations of 5 °C and 10.5 °C for plastics and elastomers were needed, respectively. The sorting efficiency was independent of particle sizes but depended on the power and time of the microwave irradiation. Generally, more than 75% (mass) of the tested polymer materials could be successfully recognized and sorted under an irradiation power of 3 kW. Plastics were much more insensitive to microwave irradiation than elastomers. With this method, the tested mixture of the plastic group (PVC, ABS, PP) and the mixture of elastomer group (EPDM, NBR, CR, and SBR) could be fully separated with an efficiency of 100%.

Near-surface snowmelt detection on the Greenland ice sheet from FengYun-3 MWRI data

The melt extent on the Greenland ice sheet plays an important role in energy balance, and the Arctic and global climates. The micro-wave radiation imager (MWRI) is one of the major payloads of Chinese second-generation polar-orbiting meteorological satellite, FengYun-3 (FY-3), and it is similar to the special sensor microwave/image (SSM/I). The cross-polarized gradient ratio (XPGR) is mainly applied in the scanning multichannel microwave radiometer (SMMR) (18 GHz horizontal polarization (18 H) and 37 GHz vertical polarization (37 V)), the advanced microwave scanning radiometer-earth observing system (AMSR-E) (18.7 GHz horizontal polarization (18 H) and 36.5 GHz vertical polarization (36 V)) and SSM/I (19.3 GHz horizontal polarization (19 H) and 37 GHz vertical polarization (37 V)), which increases the differences between dry and wet snow. The hyperplane of support vector machine (SVM) is used to detect the melt information based on the XPGR data on the Greenland ice sheet, which has higher detection accuracy comparing with the existing threshold methods in theory. The results were compared with the SSM/I data (threshold = − 0.0154), and the results show that the proposed method (That is XPGR combining with SVM) for MWRI data is feasible for the detection of the near-surface snowmelt information on the Greenland ice sheet.

Liquid water path retrieval using the lowest frequency channels of Fengyun-3C Microwave Radiation Imager (MWRI)

The Microwave Radiation Imager (MWRI) on board Chinese Fengyun-3 (FY-3) satellites provides measurements at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz with both horizontal and vertical polarization channels. Brightness temperature measurements of those channels with their central frequencies higher than 19 GHz from satellite-based microwave imager radiometers had traditionally been used to retrieve cloud liquid water path (LWP) over ocean. The results show that the lowest frequency channels are the most appropriate for retrieving LWP when its values are large. Therefore, a modified LWP retrieval algorithm is developed for retrieving LWP of different magnitudes involving not only the high frequency channels but also the lowest frequency channels of FY-3 MWRI. The theoretical estimates of the LWP retrieval errors are between 0.11 and 0.06 mm for 10.65- and 18.7-GHz channels and between 0.02 and 0.04 mm for 36.5- and 89.0-GHz channels. It is also shown that the brightness temperature observations at 10.65 GHz can be utilized to better retrieve the LWP greater than 3 mm in the eyewall region of Super Typhoon Neoguri (2014). The spiral structure of clouds within and around Typhoon Neoguri can be well captured by combining the LWP retrievals from different frequency channels.

A Model for Inversing Sea Surface Wind Speeds by Using Microwave Radiation Imager Brightness Temperatures

Based on the microwave radiative transfer theory, this thesis simulates the brightness temperature measured by Microwave Radiation Imager(MWRI) flown aboard Chinese FY-3B meteorological satellite, a new semi-statistical model for the development of the inversion algorithm is established through comparison of the simulated results with the simultaneous satellite measurements, and the MWRI five channels are log-linearly combined to inverse the real-time sea surface wind speeds under conditions of no precipitation. The results are compared to the ocean buoy measurements during June to August in 2011, shows small differences.

Improving the Spatial Resolution of FY-3 Microwave Radiation Imager via Fusion With FY-3/MERSI

This paper proposes an effective approach to improve the spatial resolution of FengYun-3 (FY-3) microwave radiation imager (MWRI) data via fusing with FY-3 medium-resolution imager (MERSI) data. Located onboard the same satellite FY-3, the complementary properties of MWRI and MERSI, such as cloud-penetrating ability and high spatial resolution, are explored to extend the applications of MWRI data to mesoscale or microscale. To this end, we make efforts to improve the spatial resolution of MWRI data by combining the spatial information and spectral information from MERSI and MWRI, respectively. The proposed fusion procedure includes two stages. In the first stage, the MERSI and MWRI images are jointly spectrally unmixed via learning a spectral dictionary pair, and then the spatial information from MERSI is transferred into MWRI by sparse coding, resulting in a spatial resolution enhanced MWRI image. In the second stage, the spectral information of the spatial resolution enhanced MWRI image is enhanced through guided filtering. To form a unified fusion framework for both the cloud-free and cloud-contaminated cases in MERSI images, we propose to leverage a learning-based single image super-resolution method in the cloud-contaminated case, which learns a spatial dictionary pair from the MWRI image pairs of low and high spatial resolutions obtained in the cloud-free case. To the best of our knowledge, the proposed method is the first fusion based one for spatial resolution enhancement of microwave radiometer images. Finally, to assess the performance of the proposed fusion framework, we choose three MERSI–MWRI image pairs to evaluate in both the cloud-free and cloud-contaminated cases. Both visual comparisons and quantitative evaluations validate the effectiveness of the proposed fusion method in preserving the spatial information of MERSI and the spectral information of MWRI. Comparisons with a state-of-the-art method that does not resort to optical data, demonstrate the superiority of the proposed method with better overall measurements for experimental results.

Evaluation of sea surface temperature from FY-3C data

ABSTRACTThe daily sea surface temperatures (SSTs) derived from the visible and infrared scanning radiometer (VIRR) and microwave radiation imager (MWRI) aboard the Fengyun-3C (FY-3C) satellite are evaluated against the measurements of in-situ SST and Reynolds optimum interpolation daily 0.25° SST (OISST) products. Statistical results for 2015 reveal a mean bias ± standard deviation of error () for the daytime VIRR SST, night-time VIRR SST, daytime MWRI SST (MWRID), and night-time MWRI SST (MWRIN) of 0.5877 ± 1.3279°C, −0.4801 ± 1.2588°C, 0.6044 ± 3.9064°C, and 0.7653 ± 3.7307°C, respectively. According to the SST bias performance of each product for different periods and latitudes, it is clear that the SST biases of the VIRR products are the largest at low latitudes and are relatively lower at mid-high latitudes and that negative SST biases in the VIRR increase with varying time. Large abnormal SST biases from MWRI products occur in Time III (the 211th to 319th day of 2015), and the average values of the SST biases are 3.6010°C and 3.7822°C for the MWRID and MWRIN, respectively. We expect that our validation results for VIRR and MWRI products can help algorithm developers further enhance the accuracy of SST retrievals while also helping sensor designers improve the performance of sensors for the next generation of FY satellites.

The Evaluation of Single-Sensor Surface Soil Moisture Anomalies over the Mainland of the People’s Republic of China

In recent years, different space agencies have launched satellite missions that carry passive microwave instruments on-board that can measure surface soil moisture. Three currently operational missions are the Soil Moisture and Ocean Salinity (SMOS) mission developed by the European Space Agency (ESA), the Advanced Microwave Scanning Radiometer 2 (AMSR2) developed by the Japan Aerospace Exploration Agency (JAXA), and the Microwave Radiation Imager (MWRI) from China’s National Satellite Meteorological Centre (NSMC). In this study, the quality of surface soil moisture anomalies derived from these passive microwave instruments was sequentially assessed over the mainland of the People’s Republic of China. First, the impact of a recent update in the Land Parameter Retrieval Model (LPRM) was assessed for MWRI observations. Then, the soil moisture measurements retrieved from the X-band observations of MWRI were compared with those of AMSR2, followed by an internal comparison of the multiple frequencies of AMSR2. Finally, SMOS retrievals from two different algorithms were also included in the comparison. For each sequential step, processing and verification chains were specifically designed to isolate the impact of algorithm (version), observation frequency or instrument characteristics. Two verification techniques are used: the statistical Triple Collocation technique is used as the primary verification tool, while the precipitation-based Rvalue technique is used to confirm key results. Our results indicate a consistently better performance throughout the entire study area after the implementation of an update of the LPRM. We also find that passive microwave observations in the AMSR2 C-band frequency (6.9 GHz) have an advantage over the AMSR2 X-band frequency (10.7 GHz) over moderate to densely vegetated regions. This finding is in line with theoretical expectations as emitted soil radiation will become masked under a dense canopy with stricter thresholds for higher passive microwave frequencies. Both AMSR2 and MWRI make X-band observations; a direct comparison between them reveals a consistently higher quality obtained by AMSR2, specifically over semi-arid climate regimes. Unfortunately, Radio Frequency Interference hampers the usefulness of soil moisture products for the SMOS L-band mission, leading to a significantly reduced revisit time over the densely populated eastern part of the country. Nevertheless, our analysis demonstrates that soil moisture products from a number of multi-frequency microwave sensors are credible alternatives for this dedicated L-band mission over the mainland of the People’s Republic of China.

Detection and analysis of television frequency interference from an FY-3C microwave radiation imager

Detection and analysis of television frequency interference from an FY-3C microwave radiation imager

Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau

Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, especially over the Tibetan Plateau (TP) which is climatically and hydrologically sensitive as the Earth’s ‘third pole’. Large-scale spatially consistent and temporally continuous soil moisture datasets are of great importance to meteorological and hydrological applications, such as weather forecasting and drought monitoring. The Fengyun-3B Microwave Radiation Imager (FY-3B/MWRI) soil moisture product is a relatively new passive microwave product, with the satellite being launched on November 5, 2010. This study validates and reconstructs FY-3B/MWRI soil moisture across the TP. First, the validation is performed using in situ measurements within two in situ soil moisture measurement networks (1°×1° and 0.25°×0.25°), and also compared with the Essential Climate Variable (ECV) soil moisture product from multiple active and passive satellite soil moisture products using new merging procedures. Results show that the ascending FY-3B/MWRI product outperforms the descending product. The ascending FY-3B/MWRI product has almost the same correlation as the ECV product with the in situ measurements. The ascending FY-3B/MWRI product has better performance than the ECV product in the frozen season and under the lower NDVI condition. When the NDVI is higher in the unfrozen season, uncertainty in the ascending FY-3B/MWRI product increases with increasing NDVI, but it could still capture the variability in soil moisture. Second, the FY-3B/MWRI soil moisture product is subsequently reconstructed using the back-propagation neural network (BP-NN) based on reconstructed MODIS products, i.e., LST, NDVI, and albedo. The reconstruction method of generating the soil moisture product not only considers the relationship between the soil moisture and NDVI, LST, and albedo, but also the relationship between the soil moisture and four-dimensional variations using the longitude, latitude, DEM and day of year (DOY). Results show that the soil moisture could be well reconstructed with R2 higher than 0.56, RMSE less than 0.1cm3cm−3, and Bias less than 0.07cm3cm−3 for both frozen and unfrozen seasons, compared with the in situ measurements at the two networks. Third, the reconstruction method is applied to generate surface soil moisture over the TP. Both original and reconstructed FY-3B/MWRI soil moisture products could be valuable in studying meteorology, hydrology, and ecosystems over the TP.

A method and its retrieval application for collocating the FY-3 microwave and VIS/IR data

As the second generation polar-orbiting meteorological satellite in China, FY-3 has been in operation for more than 7 years. Among the several Earth observing instruments, FY-3 is equipped with the Micro-Wave Radiation Imager (MWRI) and the Visible and Infrared Radiometer (VIRR), which can observe the global atmosphere, land, and ocean. A combined use of the multi-sensor remote sensing observations from FY-3 will enable us to extract comprehensive information of an Earth target. The multi-channel MWRI has a good ability to detect clouds and precipitation, although its spatial resolution changes with frequency. All VIRR channels have a high spatial resolution, but they can only obtain the information on the surface of a target. To get microwave signal at each VIRR pixel and enhance the calculated resolution of MWRI, in this study, a method is developed based on the principle of multi-information collocating, so that brightness temperature at each MWRI channel is collocated to a VIRR pixel.

Snow Depth Inversion Using the Localized HUT Model Based on FY-3B MWRI Data in the Farmland of Heilongjiang Province, China

Snow depth parameter inversion in the farmland using passive microwave remote sensing is of great significance to the agricultural production in Northeast China. Firstly, the Helsinki University of Technology (HUT) snow emission model was validated in the farmland based on microwave radiation imager (MWRI) onboard FengYun-3B satellite (FY-3B). The results showed that there was a big difference between the brightness temperature of HUT model simulation and MWRI for 18.7 GHz horizontal polarization (18.7 H) and 36.5 GHz horizontal polarization (36.5 H). To improve HUT model, the empirical parameter in the model was localized. Then the localized HUT (LHUT) model was built, where the extinction coefficient was calculated by the new extinction coefficient formula. Next, LHUT model was validated based on MWRI data and compared with HUT model. The results showed that LHUT underestimates slightly the brightness temperature with 0.91 and 4.19 K for 18.7 and 36.5 H respectively, and LHUT is superior to HUT model. Finally, the genetic algorithm (GA) was used to invert snow depth based on LHUT. The results showed that snow depth was underestimated with 6.79 cm based on LHUT. The inverted snow depth based on LHUT model is in better agreement with the measured snow depth.

Estimation and Correction of Geolocation Errors in FengYun-3C Microwave Radiation Imager Data

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.