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This study conducts a one-month parallel experiment to compare the results of the operational Shanghai Typhoon Model with (WV123) and without (CTL) the assimilation of clear-sky brightness temperature from three Fengyun-4B (FY-4B) Advanced Geostationary Radiation Imager (AGRI) water-vapor (WV) channels. The results demonstrate a notable improvement (∼3%) in the moisture fields by the WV123 experiment at all atmospheric levels in the forecasts of 24–120 h when compared to those of the CTL experiment. Besides, results show an improvement in the correlation coefficient of the 500-hPa temperature field from WV123 experiment than CTL experiment after 24 h. For Typhoon Doksuri (2023), the WV123 experiment shows a reduction of 19.4% in the mean track errors after 24-h and an improvement of approximately 10% in the forecasted intensities with five different initial forecasting times. In the cases of Typhoons Mawar, Khanun, and Kaihui in 2023, track errors also exhibit some degree of amelioration in forecast experiments at different initial forecasting times. Moreover, the WV123 experiment can improve the forecast accuracy for precipitation exceeding 250 mm in terms of both spatial distribution and intensity. A statistical study over one-month period indicates that the WV123 experiment shows a relatively neutral impact on the precipitation forecasts of 0–48 h. However, for the forecast lead time of 48–120 h, there is a considerable improvement in terms of the threat scores of predicting the precipitation exceeding 50 mm, suggesting the positive effects of assimilating clear-sky and future all-sky FY-4B AGRI WV radiances.

Abstract A factor of 2 difference is found between microwave brightness temperature (TB) observations and simulations from vector radiative transfer models. A physical explanation of this difference is related to the calibration process. In principle, for the calibration of polarimetric instruments of a total power radiometer, the radiation should be only half of the values derived from Planck function but in the actual calibration process, a full value is used. When this full value is converted to a TB it is approximately, but not exactly, twice the TB simulated by a vector radiative transfer model. This inconsistency is verified by simulations of three conically scanning microwave imagers (WindSat, AMSR2 and MWRI) and one cross‐track scanning microwave sounder (AMSU‐A). This difference cannot be compensated simply by dividing a factor of 2 to the processed TBs, especially at high frequencies or at low physical temperatures due to the nonlinear property of Planck function.

Fengyun-3F (FY-3F) satellite was launched in 2023 with a MicroWave Temperature Sounder (MWTS) and a MicroWave Humidity Sounder (MWHS) onboard. This study evaluates the in-orbit performances of these two instruments and compares them with similar instruments onboard FY-3E and NOAA-20 satellites. It is found that the polarization of FY-3F MWHS at channel 1 is different from FY-3E from the quasi-horizontal to quasi-vertical, whereas the rest of the channels are revised to quasi-horizontal polarization. FY-3F MWTS performance at the upper air channels is, in general, better than FY-3E MWTS, with 0.3 K smaller in biases (O-B) and 0.13 K lower in standard deviation. The striping noise between FY-3E and 3F MWHS is similar in magnitude for most of the channels. The FY-3F can form a satellite constellation with the FY-3E and NOAA-20, enabling better monitoring of many weather events, such as typhoons and hurricanes, through the use of all three satellites. Using the Global-Scene Dependent Atmospheric Retrieval Testbed (GSDART), Typhoon Yagi warm cores are retrieved from both MWTS/MWHS and ATMS. It is shown the warm core structures of Typhoon Yagi are consistent with the three satellites in terms of their magnitudes and locations.

Accurate information on microwave land surface emissivity (MLSE) is important for satellite data assimilation. In this article, a new random forest (RF) algorithm is developed for retrieving MLSE under all-sky conditions. Using Level-1 brightness temperature data from the FengYun-3D (FY-3D) microwave radiation imager in 2022, two global MLSE daily product datasets, clear-sky (FY-3D1) and clear/cloudy (FY-3D2), were obtained by using one-dimensional variational method and microwave radiative transfer method, respectively. Based on the global spatiotemporal consistency assessment, a high-quality daily MLSE training dataset for the Tibetan Plateau was selected from the two datasets. Then, ten land surface parameters from routine observation were chosen as input features to the RF model to simulate the MLSE under all-sky conditions in the Tibetan Plateau. The results show that both FY-3D1 and FY-3D2 MLSE datasets are comparable to the international mainstream MLSE products in quality, while the clear sky FY-3D1 is likely to be better than the clear/cloudy FY-3D2 MLSE. Land surface roughness, vegetation optical thickness, normalized vegetation index, and land cover type are identified as the most important factors affecting MLSE in the Tibetan Plateau. The RF model can effectively simulate the MLSE in the frequency range of 10.65–89.0 GHz under all-sky conditions. The coefficients of determination (R2) for horizontal polarization and vertical polarization range from 0.86 (10.65 GHz) to 0.91 (18.7 GHz) and from 0.60 (10.65 GHz) to 0.74 (89.0 GHz), respectively. The root mean square errors for horizontal polarization and vertical polarization range from 0.017 (23.8 GHz) to 0.023 (10.65 GHz) and from 0.016 (10.65 GHz) to 0.019 (89.0 GHz), respectively. These results indicate that machine learning is likely to be an effective method for future all-sky simulation of MLSE.

A polarized bidirectional reflectance distribution function (pBRDF) matrix was developed based on the two-scale roughness theory to provide consistent simulations of fully polarized microwave emission and scattering, required for the ocean–atmosphere-coupled radiative transfer model. In this study, the potential of the two-scale pBRDF matrix was explored for simulating ocean full-polarization backscattering and bistatic-scattering normalized radar cross sections (NRCSs). Comprehensive numerical simulations of the two-scale pBRDF matrix across the L-, C-, X-, and Ku-bands were carried out, and the simulations were compared with experimental data, classical electromagnetic, and GMFs. The results show that the two-scale pBRDF matrix demonstrates reasonable dependencies on ocean surface wind speeds, relative wind direction (RWD), geometries, and frequencies and has a reliable accuracy in general. In addition, the two-scale pBRDF matrix simulations were compared with the observations from the advanced scatterometer (ASCAT) onboard MetOP-C satellites, with a correlation coefficient of 0.9634 and a root mean square error (RMSE) of 2.5083 dB. In the bistatic case, the two-scale pBRDF matrix simulations were compared with Cyclone Global Navigation Satellite System (CYGNSS) observations, demonstrating a good correlation coefficient of 0.8480 and an RMSE of 1.2859 dB. In both cases, the two-scale pBRDF matrix produced fairly good simulations at medium-to-high wind speeds. The relatively large differences at low wind speeds (

Abstract Global surface snowfall rate estimation is crucial for hydrological and meteorological applications but is still a challenging task. A novel approach is developed to comprehensively use passive microwave, infrared data and physical constraints in deep‐learning neural networks with an attention module for retrieving surface snowfall rate (PCSSR‐DNNWA). The PCSSR‐DNNWA consistently outperforms traditional approaches in predicting surface snowfall rates with a correlation coefficient of ∼0.76, mean error of ∼−0.02 mm/hr, and root mean squared error of ∼0.21 mm/hr. It is found that graupel water path is of vital importance with largest contributions in retrieving surface snowfall rate. By integrating the physical constraints, the algorithm of PCSSR‐DNNWA opens a new avenue for retrieving the surface snowfall rate from satellites since some predictors are intelligently considered, resulting in an increased accuracy, interpretability, and computational efficiency.

Abstract Depicting Secondary Eyewall Formation (SEF) and Eyewall Replacement Cycle (ERC) in a numerical model is important for tropical cyclone (TC) forecasting. However, there is no consensus about what resolutions are appropriate to describe SEF/ERC within a full‐physics mesoscale model. In this study, numerical experiments are conducted to examine the impact of the horizontal and vertical resolutions on SEF/ERC. The mesoscale model is configured through nesting to the horizontal grid spacings of 6, 4, 2, 1.33, 0.67‐km, and with 27‐ and 54‐levels on an f‐plane in a quiescent environment. In addition, there are more levels below 1.5‐km to better describe the TC boundary layer (TCBL). The simulations with 6 and 4‐km grid spacings show no obvious SEF/ERC regardless of the number of vertical levels. When the horizontal grid spacings decrease to 2‐km or smaller, the simulations manifest SEF/ERC. These results are supported by a few simulations with the ARW model using similar configurations. Furthermore, the spectra of kinetic energy and vertical velocity from various resolutions confirm that the grid spacings should be smaller than 4‐km to resolve SEF/ERC. The impact of doubling vertical levels on the SEF/ERC is not as significant as doubling the horizontal resolutions. Finally, we discuss the coupling between the balanced/unbalanced flows (above/in the TCBL), and their effect on SEF. It is proposed that the coupled balanced/unbalanced processes that generate the quasi‐steady cooling zone in the primary eyewall and two warming regions inside and beyond the cooling zone are essential for SEF.

In this paper, the Advanced Weather Research Forecast model (WRF-ARW) is used to investigate the potential impacts of assimilating the FengYun-4A (FY-4A) Geostationary Interferometric Infrared Sounder (GIIRS) long-wave temperature sounding channel data on the prediction of Typhoon In-Fa (2021). In addition, a series of data assimilation experiments are conducted to demonstrate the added value of the FY-4A/GIIRS data assimilation for typhoon forecasts. It is shown that the higher spectral resolution and broader coverage of GIIRS radiance data can positively impact the model analysis and forecasts with larger temperature and moisture increments at the initial time of simulations, thus producing a better simulation for the typhoon’s warm core aloft, vortex wind structure, and spiral rainfall band. Moreover, the assimilation of the GIIRS data can also lead to better storm steering flows and, consequently, better typhoon track forecasts. Overall, the assimilation of FY-4A/GIIRS temperature sounding channel data shows some added values to improve the track and storm structure forecasts of Typhoon In-Fa.

The brightness temperatures and their Jacobians with respect to atmospheric temperature and humidity at the microwave to millimeter wave spectral bands from 23 GHz to 424 GHz are simulated with the ECMWF IFS-137 profiles as inputs to the Advanced Radiative transfer Modeling System (ARMS). The information content of temperature and humidity is then calculated individually through the Shannon entropy which is contributed by a-priori background information and observations. For a typical set of measurement uncertainties, a high information content for atmospheric temperature is mainly obtained from V band near 50–70 GHz, whereas that for water vapor comes from G band near 183 GHz and Y1 band near 380 GHz. The channels within the G band have a large temperature information content mainly for lower and middle layers of troposphere and the Y1 band has a relatively large humidity information content for the entire troposphere. A large measurement uncertainty can significantly reduce the information content of each band. Thus, to make a best use of the data from each band, it is important to reduce the instrument calibration noise and increase the accuracy in forward radiative transfer simulation.

In this study, the polarization characteristics of the newly launched Fengyun-3E (FY-3E) microwave sounding instruments are discussed, and its data quality is also assessed using one month of observation by the double-difference method. By comparison with the equivalent channels onboard Fengyun-3D (FY-3D) and advanced technology microwave sounder (ATMS), the data quality of FY-3E Microwave Humidity Sounder-II (MWHS-II) is improved and comparable to ATMS, while the data of FY-3E Microwave Temperature Sounder-III (MWTS-III) are slightly worse than data of FY-3D. The data of FY-3E MWTS-III are more susceptible to the early-morning orbit than the data of MWHS-II. In addition, striping noise is still present in channels 5–10 of MWTS-III. After the assessments, FY-3E microwave data are preprocessed and assimilated in the global forecast system for the Chinese Meteorology Administration (CMA-GFS). A total of six individual experiments over the period from 16 July to 15 August 2021 were conducted and the impact was evaluated with the composite score used in operation. It is shown that not only the forecasts for the southern hemisphere and tropics are improved significantly, but also the predictions for the northern hemisphere show some improvements in an overall neutral change from adding FY-3E microwave sounding instruments. The impact of FY-3E microwave radiance is equivalent to ATMS as they are assimilated individually. Furthermore, we note that the forecast impact is affected by the cloud detection scheme to a large extent.

Combining the temperature sounding channels near 118 GHz onboard Fengyun-3D (FY-3D) with channels near 50 GHz makes it possible to obtain the spatial and vertical distributions of the clouds through a cloud emission and scattering index (CESI). Previous research has shown its advantages in cloud detection over oceans. In this study, the CESI method is expanded and validated under different surface conditions, and angular corrections are conducted to remove the effect of viewing angles. The landfall process of Typhoon MANGKHUT and a case over special terrain are chosen to investigate its sensitivities to different surface types. It is found that the cloud spatial distribution is well demonstrated in both of the cases. Moreover, the CESI vertical distributions are compared with the Global Precipitation Measurement (GPM) hydrometeor profiles. The results show that CESIs are highly related to the GPM hydrometeor profiles in all of the conditions, and the correlations with the sea surface vary with the weighting functions of the matched channels, while the phenomenon is not obvious for the land surface. In addition, the validation results show that the new threshold combination for different surface types at different heights can be more effective for cloud identification. The probability density distribution for most of the channels of the screened-out clear sky data approximates a Gaussian distribution well, and these radiances can be well assimilated into the numerical weather prediction models.

Sand and dust storms (SDSs) are common environmental hazards in spring in Asian continent and have significant impacts on human health, weather, and climate. While many technologies have been developed to monitor SDSs, this study investigates the spectral characteristics of SDSs in satellite hyperspectral infrared observations and propose a new methodology to monitor the storms. An SDS emission and scattering index (SESI) is based on the differential responses of infrared CO2 shortwave and longwave IR bands to the scattering and emission of sand and dust particles. For a severe dust storm process during 14–17 March 2021, the SESI calculated by the Cross-track Infrared Sounder (CrIS) observations shows very negative values in the dusty region and is consistent with the spatial distribution of dust identified from the true-color RGB imagery and the dust RGB imagery of the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA-20 Satellite. The use of the SESI index in the near-surface layer allows for monitoring of the dust storm process and enables an effective classification between surface variations and dust weather events.

For many years, the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments have been widely used to monitor the condition of surface vegetation. Since the polar-orbiting satellite provides limited daily samples on surface, a completed spatial coverage of land vegetation is often relied on over multiple days of observations. In this study, observations from the Japanese geostationary satellite imager Advanced Himawari Imagers (AHI) are used to derive the surface vegetation index. The AHI reflectance at visible and near-infrared bands are first corrected to the surface reflectance by using the 6S radiative transfer model. The AHI surface reflectance from various viewing angles and solar geometry is further normalized to form an angular-independent reflectance by using a BRDF model. Finally, the surface vegetation index is calculated and synthesized from the daytime AHI data. It is found that the high-frequency AHI observations can significantly reduce the impact of clouds on compositing land NDVI and require a shorter time for a complete coverage of surface conditions. Also, a single NDVI image from AHI exhibits spatial distribution similar to that from 16 days of MODIS data.

In this study, the regional Weather Research and Forecasting model (WRF)-based quantitative precipitation forecasts (QPFs) are conducted for an extreme Meiyu rainfall event over East Asia in 2020. The data of water vapor channels 9 and 10 from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Fengyun-4A (FY-4A) satellite are assimilated through the Gridpoint Statistical Interpolation (GSI) system. It shows that a reasonable amount of assimilated AGRI data can produce reasonable water vapor increments, compared to the too sparse or dense assimilated AGRI observations. In addition, the critical success indexes (CSIs) of the precipitation forecasts within 72 h are obviously improved. The enhanced variational bias correction (VarBC) scheme is applied to remove the air-mass and scan-angle biases, and the mean observation-minus-background (O − B) values before and after the VarBC of channel 9 are −1.185 and 0.02 K, respectively, and those of channel 10 are −0.559 and −0.01 K, respectively. Assimilating the upper-level channel 9 data of AGRI (EXP_WV9) lead to a neutral-to-positive effect on QPFs, compared to the control run (CTL), which is based on the assimilation of Advanced Microwave Sounding Unit-A (AMSU-A) data. In particular, the CSIs from 42 to 72 h are significantly improved. However, the assimilation of the AGRI channel 10 (EXP_WV10) shows a neutral-to-negative effect on QPFs in this study, probably due to the complicated surface situations. This study confirms the feasibility of assimilating the water vapor channel data of FY4A AGRI in the GSI system and highlights the importance of assimilating AGRI channel 9 data to improve the QPFs of the Meiyu rainfall event.

The observations from satellite microwave-sounding instruments have been widely used in weather and climate studies. Since the data resolution varies with frequency and satellite viewing angle, it is normally required that the measurements at each frequency be resampled to obtain a uniform resolution prior to various applications. In this study, the ATOVS and AVHRR pre-processing package (AAPP) Fourier transform algorithm is modified for ATMS data and the results are compared with those derived from Backus–Gilbert inversion (BGI) and the original AAPP. From the simulated and observed ATMS data, we demonstrated the new algorithm has better results in terms of imaging quality and noise suppression, compared with BGI and AAPP. In general, our modified AAPP algorithm reduces the error by at least about 0.5 K in ATMS channels 2 and 6 and at all the viewing angles.

Three Microwave Radiation Imagers (MWRI) were carried onboard the FengYun-3B/C/D satellites and have collected more than 10 years of data since 2010. To create a robust climate quality of data, MWRI level one data were reprocessed with new calibration. This study evaluates the performance of retrieving global soil moisture from recalibrated MWRI data (RCD) and quantifies the difference of retrieved soil moisture between operational calibration data (OCD) and RCD. Soil Moisture Operational Products System (SMOPS) products from NOAA on four days of different seasons were collocated with MWRI brightness temperatures, and then the collocated data were used for training an algorithm through machine learning. The retrieved soil moisture products using OCD and RCD were evaluated against the independent SMOPS products, in situ networks and SMAP soil moisture product. It is shown that the algorithm from the random forest is suitable for FY-3D recalibrated MWRI data, with a coefficient of determination (R2) of 0.7223, a mean bias of −0.0062 and an unbiased root mean square difference (ubRMSD) of 0.0476 m3 m−3 compared with SMOPS products over the period from 12 July 2018 to 31 December 2019. The difference of retrieved soil moisture using OCD and RCD is spatially heterogeneous. Both temporal and spatial coverage and accuracy of the existing FY-3D operational soil moisture products are significantly improved.

Cloud liquid water (CLW) and total precipitable water (TPW) are two important parameters for weather and climate applications. Typically, microwave temperature sounding instruments onboard satellites are designed with two low-frequency channels at 23.8 and 31.4 GHz and can be used for retrieving CLW and TPW over global oceans. Since MWTS-III polarization at above two frequencies is uncertain, we must first determine their polarization involved in retrieval algorithms. Through radiative transfer simulation, we found that uses of the quasi-horizontal polarization for MWTS-III can produce smaller biases between observations and simulations and the scan-angle dependence of the biases is also in a general frown pattern, which is similar to ATMS pitch-maneuver observations. After the characterization of MWTS-III polarization, CLW and TPW are derived from Microwave Temperature Sounder (MWTS-III) and are compared with those from ATMS. It is found that CLW and TPW derived from two instruments exhibit a high consistency in terms of their spatial distributions and magnitudes.

Spaceborne passive microwave sounding instruments are important for monitoring tropical cyclones (TCs) over oceans. However, previous studies have found large retrieval errors at TCs’ inner region at the lower troposphere where heavy precipitation occurs. In this study, the background error covariance matrix used in the variational retrieval algorithm is designed to vary with atmospheric conditions. It is found that the errors of retrieved temperature and humidity profiles are significantly reduced under the TC conditions, when they are compared with those from using a static covariance matrix. The retrieval errors of temperature and humidity are about 1.5 K and 10–20%, respectively, in the troposphere. Moreover, the influence of different observation operators on the retrievals are also investigated. It is shown that ARMS (Advanced Radiative Transfer Modeling System) used as an observation operator can produce a higher retrieval accuracy, compared to CRTM (Community Radiative Transfer Model). For the relative humidity profile, the error can be reduced by up to 5% from ARMS. The reason may be attributed to the more comprehensive handling of the scattering from various hydrometeors in ARMS, which results in a higher retrieval accuracy under cloudy conditions.

Atmospheric wind is an essential parameter in the global observing system. In this study, the water vapor field in Typhoon Lekima and its surrounding areas simulated by the Weather Research and Forecasting (WRF) model is utilized to track the atmospheric motion wind through the Farneback Optical Flow (OF) algorithm. A series of experiments are conducted to investigate the influence of temporal and spatial resolutions on the errors of tracked winds. It is shown that the wind accuracy from tracking the specific humidity is higher than that from tracking the relative humidity. For fast-evolving weather systems such as typhoons, the shorter time step allows for more accurate wind retrievals, whereas for slow to moderate evolving weather conditions, the longer time step is needed for smaller retrieval errors. Compared to the traditional atmospheric motion vectors (AMVs) algorithm, the Farneback OF wind algorithm achieves a pixel-wise feature tracking and obtains a higher spatial resolution of wind field. It also works well under some special circumstances such as very low water vapor content or the region where the wind direction is parallel to the moisture gradient direction. This study has some significant implications for the configuration of satellite microwave sounding missions through their derived water vapor fields. The required temporal and spatial resolutions in the OF algorithm critically determine the satellite revisiting time and the field of view size. The brightness temperature (BT) simulated through Community Radiative Transfer Model (CRTM) is also used to track winds. It is shown that the error of tracking BT is generally larger than that of tracking water vapor. This increased error may result from the uncertainty in simulations of brightness temperatures at 183 GHz.

In this study, the Fengyun-3D (FY-3D) clear-sky microwave temperature sounder-2 (MWTS-2) radiances were directly assimilated in the regional mesoscale Weather Research and Forecasting (WRF) model using the Gridpoint Statistical Interpolation (GSI) data assimilation system. The assimilation experiments were conducted to compare the track errors of typhoon Lekima from uses of the Advanced Microwave Sounding Unit-A (AMSU-A) radiances (EXP_AD) with those from FY-3D MWTS-2 upper-air sounding data at channels 5–7 (EXP_AMD). The clear-sky mean bias-corrected observation-minus-background (O-B) values of FY-3D MWTS-2 channels 5, 6, and 7 are 0.27, 0.10 and 0.57 K, respectively, which are smaller than those without bias corrections. Compared with the control experiment, which was the forecast of the WRF model without use of satellite data, the assimilation of satellite radiances can improve the forecast performance and reduce the mean track error by 8.7% (~18.4 km) and 30% (~58.6 km) beyond 36 h through the EXP_AD and EXP_AMD, respectively. The direction of simulated steering flow changed from southwest in the EXP_AD to southeast in the EXP_AMD, which can be pivotal to forecasting the landfall of typhoon Lekima (2019) three days in advance. Assimilation of MWTS-2 upper-troposphere channels 5–7 has great potential to improve the track forecasts for typhoon Lekima.

Atmospheric methane (CH4) is a standard product of the atmospheric infrared sounder (AIRS) aboard NASA's Aqua satellite, generated at the NASA Goddard Earth Sciences Data and Information Services Center (NASA/GES/DISC), and a product of the infrared atmospheric sounding interferometer (IASI) aboard METOP-A,-B, generated at National Oceanic and Atmospheric Administration's Comprehensive Large Array-data Stewardship System. In order to understand the capability of these two sensors in observing the spatial and temporal distribution of CH4, this paper compares the CH4 products from AIRS and IASI with aircraft measurements, as well as the corresponding time series in tropics and high northern latitude regions. It is found that the mean degree of freedom from AIRS is smaller than IASI by -0.049 ± 0.152, and in their peak sensitive altitude between 350 and 650 hPa their difference (AIRS - IASI) is about 2.8 ± 17.2 ppb. Both AIRS and IASI can capture the latitudinal gradient, but there is a large scattering in the high northern latitude regions. They agree well in observing the summer enhancement of CH4 during the Monsoon season over South Asia, and the seasonal cycles over Siberia (except for a relatively larger difference in the cold season). These results highlight that AIRS and IASI can provide valuable information to capture the spatiotemporal variation of CH4 in the mid-upper troposphere in most periods and regions, but it is needed to further improve the data quality to make a consistent product using both sensors.

A new database, the tropical cyclones passive microwave brightness temperature (TCsBT) database including 6273 overpasses of 503 tropical cyclones (TC) was established from 6-year (2011−2016) Fengyun-3B (FY-3B) Microwave Radiation Imager (MWRI) Level-1 brightness temperature (TB) data and TC best-track data. An algorithm to estimate the TC intensity is developed using MWRI TB’s from the database. The relationship between microwave TB and the maximum sustained surface wind (Vmax) of TCs is derived from the TCsBT database. A high correlation coefficient between MWRI channel TB and Vmax is found at the radial distance 50−100 km near the TC inner core. Brightness temperatures at 10.65, 18.70, 23.8, and 36.5 GHz increase but 89 GHz TB’s and polarization corrected TB at 36.5 GHz (PCT36.50) and PCT89 decrease with increasing TC intensity. The TCsBT database is further separated into the 5063 dependent samples (2010−2015) for the development of the TC intensity estimation algorithm and 1210 independent samples (2016) for algorithm verification. The stepwise regression method is used to select the optimal combination of storm intensity estimation variables from 12 candidate variables and four parameters (10.65h, 23.80v, 89.00v and PCT36.50) were selected for multiple regression models development. Among the four predictors, PCT36.50 contributes the most in estimating TC intensity. In addition, the errors are lower for estimating 6-h and 12-h future Vmax than estimating the current Vmax.

Satellite-derived aerosol optical depths (AODs) have been widely used to estimate surface fine particulate matter (PM2.5) concentrations over areas that do not have PM2.5 monitoring sites. To date, most studies have focused on estimating daily PM2.5 concentrations using polar-orbiting satellite data (e.g., from the Moderate Resolution Imaging Spectroradiometer), which are inadequate for understanding the evolution of PM2.5 distributions. This study estimates hourly PM2.5 concentrations from Himawari AOD and meteorological parameters using an ensemble learning model. We analyzed the spatial agglomeration patterns of the estimated PM2.5 concentrations over central East China. The estimated PM2.5 concentrations agree well with ground-based data with an overall cross-validated coefficient of determination of 0.86 and a root-mean-square error of 17.3 μg m−3. Satellite-estimated PM2.5 concentrations over central East China display a north-to-south decreasing gradient with the highest concentration in winter and the lowest concentration in summer. Diurnally, concentrations are higher in the morning and lower in the afternoon. PM2.5 concentrations exhibit a significant spatial agglomeration effect in central East China. The errors in AOD do not necessarily affect the retrieval accuracy of PM2.5 proportionally, especially if the error is systematic. High-frequency spatiotemporal PM2.5 variations can improve our understanding of the formation and transportation processes of regional pollution episodes.

The Suomi National Polar-orbiting Partnership (SNPP) satellite was launched on 28 October, 2011, and carries the Advanced Technology Microwave Sounder (ATMS) onboard. Currently, ATMS performance in orbit is very stable and the calibration parameters (e.g., noise and accuracy) meet specifications. This study documents an ATMS calibration error budget model and its results for community reference. The calibration accuracy is also verified with the ATMS pitch maneuver observations of cold space. It is shown that the ATMS pitch maneuver cold space observations at center positions are inconsistent with the values predicted by the instrument calibration error budget model. The biases also depend on scan angle. This scan-angle dependence may be caused by the ATMS plane reflector emission. Thus, a physical model is developed to simulate the radiation emitted from the reflector and is recommended as part of ATMS radiance calibration to further improve the sensor data record (SDR) data quality.

The main reflector of the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F-16 satellite emits variable radiation, and the SSMIS warm calibration load is intruded by direct and indirect solar radiation. These contamination sources produce antenna brightness temperature anomalies of around 2 K at SSMIS sounding channels which are obviously inappropriate for assimilation into numerical weather prediction models and remote sensing retrievals of atmospheric and surface parameters. In this study, antenna brightness temperature anomalies at several lower atmospheric sounding (LAS) channels are assessed, and the algorithm is developed for corrections of these antenna temperature anomalies. When compared against radiative transfer model simulations and simultaneous observations from AMSU-A aboard NOAA-16, the SSMIS antenna temperatures at 52.8, 53.6, 54.4, 55.5, 57.3, and 59.4 GHz after the anomaly correction exhibit small residual errors (

The Suomi NPP (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) performs the scheduled lunar roll maneuver on a monthly basis. The lunar calibration coefficients and lunar F-factor are calculated by taking the ratio of the lunar observed radiance to the simulated radiance from the Miller and Turner (MT) lunar model. The lunar F-factor is also validated against that derived from the VIIRS Solar Diffuser (SD). The MT model-based lunar F-factors in general agree with SD F-factors. The Lunar Band Ratio (LBR) is also derived from two channel lunar radiances and is implemented in the National Oceanic and Atmospheric Administration (NOAA) Integrated Calibration and Validation System (ICVS) to monitor the VIIRS long-term radiometric performance. The lunar radiances at pixels are summed for each of the VIIRS Reflective Solar Bands (RSBs) and normalized by the reference band M11 which has the most stable SD-based calibration coefficient. LBRs agree with the SD based F-factor ratios within one percent. Based on analysis with these two independent lunar calibration methods, SD-based and LBR-based calibrations show a lifetime consistency. Thus, it is recommended that LBR be used for both VIIRS radiometric calibration and lifetime stability monitoring.