Window Channels Research Articles

Using GOES ABI Split-Window Radiances to Retrieve Daytime Low-Level Water Vapor for Convective Forecasting

A clear sky, daytime retrieval of low-level precipitable water vapor based on the single-layer approximation to the radiative transfer equation applied to three longwave window channels is described. The algorithm, which simultaneously retrieves skin temperature and air temperature as well, has the advantage of using satellite radiances only—no ancillary information is needed. Application of the method to Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI) radiance observations at 10.3, 11.2 and 12.3 µm demonstrates its utility in monitoring the location of low-level water vapor in the warm season, pre-convective atmosphere over the United States.

A New Methodology on Noise Equivalent Differential Temperature Calculation for On-Orbit Advanced Microwave Sounding Unit-A Instrument

A few deficiencies remain in the current National Oceanic and Atmospheric Administration (NOAA) Integrated Calibration/Validation System (ICVS) noise calculation method for characterizing noise equivalent differential temperature <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(NE\Delta T)$ </tex-math></inline-formula> performance of on-orbit Advanced Microwave Sounding Unit-A (AMSU-A) instruments. This ICVS method only accounts for the noise contribution resulting from one calibration parameter (warm count). The calculation is also dependent upon an assumption that the calibration gain equals to the sensitivity of warm count to temperature. In addition, the dependence of scene temperature is not considered. This study establishes a new methodology by accounting for the noise components resulting from all calibration parameters such as warm counts, warm target temperatures, space view (cold) counts, and their covariance. Each noise component is computed using the product of the overlapping Allan deviation of corresponding parameter and the sensitivity of Earth scene temperature to the parameter. The new method also comprises the variation of scene temperature via scene count in noise estimation. For the AMSU-A instruments aboard the NOAA-18 to NOAA-19 and Metop-A to Metop-C satellites, the magnitudes of the orbital average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$NE\Delta T$ </tex-math></inline-formula> calculated using the ICVS method exceed those from the new method by approximately 8%–38%, corresponding to a range from 0.02 to 0.07 K. Particularly, the deviations at the upper temperature sounding channels from 10 to 14 are around 0.05 K. The magnitude of AMSU-A instrument noise can vary by around 18% for sounding channels and 40% for window channels due to scene temperature change. Therefore, the new method demonstrates its significant improvements in characterizing on-orbit AMSU-A instrument noise more accurately and comprehensively.

Typhoon Warm-Core Structures Derived from FY-3D MWTS-2 Observations

In this study, the three-dimensional (3D) warm-core structures of the Northwest Pacific typhoons Francisco, Lekima, and Krosa in August 2019 are retrieved from the Fengyun-3D (FY-3D) microwave temperature sounder-2 (MWTS-2) observations of brightness temperature. Due to the lack of two window channels at 23.8 GHz and 31.4 GHz, an empirical cloud detection algorithm based on 50.3 GHz bias-corrected observations-minus-backgrounds is applied to obtain clear-sky observations for the multiple linear regression retrieval algorithm. The MWTS-2 cloud-affected channels 3–5 are not used to retrieve temperatures under cloudy conditions to eliminate low-tropospheric cold anomalies. The multiple linear regression coefficients are obtained based on MWTS-2 brightness temperatures and the temperatures from the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5) in the training period of three weeks before the month of targeted typhoons. The proposed MWTS-2 warm-core retrieval can well capture the radial and vertical temporal evolutions of the temperature anomalies of the typhoons Francisco, Lekima, and Krosa. The sizes of the warm-core anomalies of typhoons Lekima and Krosa retrieved by the MWTS-2 are horizontally and vertically similar to and stronger than those of the ERA5. Compared with the ERA5 reanalysis in August 2019, the biases for MWTS-2 temperature retrievals are smaller than ±0.25 K, with root-mean-square errors (RMSEs) smaller than and 2.0 K at all altitudes. Additionally, the location of the 250-hPa maximum temperature anomaly retrieved by the MWTS-2 is closer to the best track than that of the ERA5. A weak warm-core around 200 hPa and a cold-core anomaly in the middle troposphere are also found in the outer rain bands region due to the effect of evaporation of rainfall.

A New 32-Day Average-Difference Method for Calculating Inter-Sensor Calibration Radiometric Biases between SNPP and NOAA-20 Instruments within ICVS Framework

Two existing double-difference (DD) methods, using either a 3rdSensor or Radiative Transfer Modeling (RTM) as a transfer, are applicable primarily for limited regions and channels, and, thus critical in capturing inter-sensor calibration radiometric bias features. A supplementary method is also desirable for estimating inter-sensor calibration biases at the window and lower sounding channels where the DD methods have non-negligible errors. In this study, using the Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System (JPSS)-1 (alias NOAA-20) as an example, we present a new inter-sensor bias statistical method by calculating 32-day averaged differences (32D-AD) of radiometric measurements between the same instrument onboard two satellites. In the new method, a quality control (QC) scheme using one-sigma (for radiance difference), or two-sigma (for radiance) thresholds are established to remove outliers that are significantly affected by diurnal biases within the 32-day temporal coverage. The performance of the method is assessed by applying it to estimate inter-sensor calibration radiometric biases for four instruments onboard SNPP and NOAA-20, i.e., Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS), Nadir Profiler (NP) within the Ozone Mapping and Profiler Suite (OMPS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Our analyses indicate that the globally-averaged inter-sensor differences using the 32D-AD method agree with those using the existing DD methods for available channels, with margins partially due to remaining diurnal errors. In addition, the new method shows its capability in assessing zonal mean features of inter-sensor calibration biases at upper sounding channels. It also detects the solar intrusion anomaly occurring on NOAA-20 OMPS NP at wavelengths below 300 nm over the Northern Hemisphere. Currently, the new method is being operationally adopted to monitor the long-term trends of (globally-averaged) inter-sensor calibration radiometric biases at all channels for the above sensors in the Integrated Calibration/Validation System (ICVS). It is valuable in demonstrating the quality consistencies of the SDR data at the four instruments between SNPP and NOAA-20 in long-term statistics. The methodology is also applicable for other POES cross-sensor calibration bias assessments with minor changes.

Evaluation of synthetic satellite images computed from radiative transfer models over a region of South America using WRF and GOES‐13/16 observations

AbstractSynthetic infrared GOES‐13 observations are generated from a high‐resolution (4 km) Weather Research and Forecasting (WRF) model run over Argentina for a meteorological event of deep moist convection using two different radiative transfer models. The fast operational Community Radiative Transfer Model (CRTM) and the physics‐based research model Atmospheric Radiative Transfer Simulator (ARTS) are compared with the available observations. CRTM shows good results at a low computational cost, and is a good candidate for operational use in the region. CRTM and ARTS synthetic satellite images show differences due to the treatment of the bulk scattering properties of the frozen hydrometeor species, especially around the cloud shield. With this WRF+CRTM configuration, a long‐term evaluation is conducted over 12 hr forecasts from one month of data in a region of the South American autumn with four different initializations at 0000, 0600, 1200 and 1800 UTC, as operational at the National Meteorological Service of Argentina. The simulated and observed GOES‐16 Advanced Baseline Imager (ABI) brightness temperatures (BTs) in the 6.9 m channel show good agreement with an averaged bias of K (RMSE 21.7 K), whereas the performances in the IR window channels show slightly larger averaged BT differences of 3.5 K (RMSE 15.2 K). BT differences between the 6.9 and 10.3 m channels indicate that the WRF+CRTM simulations underestimate low to mid‐level clouds and to a lesser extent high‐level clouds. In the BT (10.3 m) range between approximately 215 and 230 K, there is an overestimation of clouds with BT differences above 0 K between 6.9 and 10.3 m. This could be due to the misrepresentation of upper‐level clouds.

Estimation of the error covariance matrix for IASI radiances and its impact on the assimilation of ozone in a chemistry transport model

Abstract. In atmospheric chemistry retrievals and data assimilation systems, observation errors associated with satellite radiances are chosen empirically and generally treated as uncorrelated. In this work, we estimate inter-channel error covariances for the Infrared Atmospheric Sounding Interferometer (IASI) and evaluate their impact on ozone assimilation with the chemistry transport model MOCAGE (Modèle de Chimie Atmosphérique à Grande Echelle). The method used to calculate observation errors is a diagnostic based on the observation and analysis residual statistics already adopted in many numerical weather prediction centres. We used a subset of 280 channels covering the spectral range between 980 and 1100 cm−1 to estimate the observation-error covariance matrix. This spectral range includes ozone-sensitive and atmospheric window channels. We computed hourly 3D-Var analyses and compared the resulting O3 fields against ozonesondes and the measurements provided by the Microwave Limb Sounder (MLS) and by the Ozone Monitoring Instrument (OMI). The results show significant differences between using the estimated error covariance matrix with respect to the empirical diagonal matrix employed in previous studies. The validation of the analyses against independent data reports a significant improvement, especially in the tropical stratosphere. The computational cost has also been reduced when the estimated covariance matrix is employed in the assimilation system, by reducing the number of iterations needed for the minimizer to converge.

The Impact of Aerosols on Satellite Radiance Data Assimilation Using NCEP Global Data Assimilation System

Aerosol radiative effects have been studied extensively by climate and weather research communities. However, aerosol impacts on radiance in the context of data assimilation (DA) have received little research attention. In this study, we investigated the aerosol impacts on the assimilation of satellite radiances by incorporating time-varying three-dimensional aerosol distributions into the radiance observation operator. A series of DA experiments was conducted for August 2017. We assessed the aerosol impacts on the simulated brightness temperatures (BTs), bias correction and quality control (QC) algorithms for the assimilated infrared sensors, and analyzed temperature fields. We found that taking the aerosols into account reduces simulated BT in thermal window channels (8 to 13 μm) by up to 4 K over dust-dominant regions. The cooler simulated BTs result in more positive first-guess departures, produce more negative biases, and alter the QC checks about 20%/40% of total/assimilated observations at the wavelength of 10.39 μm. As a result, assimilating aerosol-affected BTs produces a warmer analyzed lower atmosphere and sea surface temperature which have better agreement with measurements over the trans-Atlantic region.

Retrieval of cloud-cleared radiances using numerical weather prediction model-analysis and forecast fields for INSAT-3D sounder longwave window channel observations

Estimation of cloud-cleared radiances, without utilizing any supplementary sensors for INSAT-3D Sounder InfraRed window observations is presented here. This method utilizes simulated clear sky radiances by radiative transfer for TOVS (RTTOV) tool, with the help of global numerical weather prediction (NWP) models analysis and forecast fields, unlike the previously reported approaches where observations from auxiliary/collocated sensors are used. The validation of these derived cloud-cleared radiances is carried out with the already existing operational Level 2 (L2) cloud-cleared radiances of Atmospheric Infra-Red Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI), where the retrieval is carried out utilizing auxiliary Microwave Sounders. Also, a comparative study with the optimal cloud-clearing technique has been carried out here, where the retrieval is done by utilizing the clear sky radiances by means of collocated high-resolution InfraRed observations with the help of auxiliary sensors. The results indicate the scope and promising potential of this technique towards the future operational endeavor of deriving the cloud-cleared radiances without any assistance of co-existing auxiliary hardware system.

Assimilation of MWHS-2/FY-3C 183 GHz Channels Using a Dynamic Emissivity Retrieval and Its Impacts on Precipitation Forecasts: A Southwest Vortex Case

The dynamic emissivity retrieved from window channels of the microwave humidity sounder II (MWHS-2) onboard the China Meteorological Administration’s FengYun (FY)-3C polar orbiting satellite can provide more realistic emissivity over lands and potentially improve the numerical weather prediction (NWP) forecasts. However, whether the assimilation with the dynamic emissivity works for the precipitation forecasts over the complex geography is less investigated. In this paper, a typical precipitating case generated by the Southwest Vortex is selected and the Weather Research and Forecasting data assimilation (WRFDA) system is applied to examine the impacts of assimilating MWHS-2/FY-3C with the uses of the emissivity atlas and the dynamic emissivity on the forecasts. The results indicate that the use of the dynamic emissivity retrieved from the 89 GHz channel of MWHS-2/FY-3C apparently increases the used data number for assimilation and does improve the initial fields and the 24-hour forecasts (from 0000 UTC 24 June 2016 to 0000 UTC 25 June 2016) of precipitation distribution and intensity except for the rainfall over 100 mm. But these positive impacts are not evidently better than those with the emissivity atlas. In general, these results still suggest that the future use of the dynamic emissivity in the assimilation over the complex terrain is promising.

An evaluation of the synergy of satellite passive microwave observations between 1.4 and 36 GHz, for vegetation characterization over the Tropics

Satellite passive microwave observations from 1.4 to 36 GHz already showed sensitivity to vegetation parameters, primarily through the calculations of the Vegetation Optical Depth (VOD) at individual window frequencies, separately. Here we evaluate the synergy of this frequency range for vegetation characterization, through the estimation of two vegetation parameters, its foliage and the photosynthesis activity as described by the Normalized Difference Vegetation Index (NDVI), and its woody components and carbon stock as described by the Above Ground Carbon (AGC), using different combinations of channels in the considered frequency range. Neural network retrievals are trained on these two vegetation parameters (NDVI and AGC), for several microwave channel combinations, including the future Copernicus Imaging Microwave Radiometer (CIMR) that will observe simultaneously in window channels from 1.4 to 36 GHz, for the first time. This methodology avoids the use of any assumptions in the complex interaction between the surface (vegetation and soil) and the radiation, as well as any ancillary observations, to propose a genuine and objective evaluation of the information content of the passive microwave frequencies for vegetation characterization. Our analysis quantifies the synergy of the microwave frequencies from 1.4 to 36 GHz, and shows the expected potential of the CIMR instrument for the monitoring of vegetation parameters in tropical environments, as compared to current instruments. It also confirms that the 1.4 GHz observations have a high sensitivity to AGC, as compared to the other single frequencies up to 36 GHz, at least under tropical environments.

A Novel Detection Algorithm for Impulse Spike Interferences in Cross-Track Infrared Sounder Short-Wave Spectral Radiance

Due to deficiencies in the operational spike detection, many moderate and strong impulse spike interferences constantly remain nondetected in the cross-track infrared sounder (CrIS) sensor data record (SDR) data at the short-wave infrared (SWIR) band over the South Atlantic Anomaly (SAA) region. This study characterizes imaginary radiance spectrum features in the presence of four intensities of spikes at three different hitting locations through radiative transfer model simulations. It is found that periodic ringing patterns appear in the imaginary spectral radiance. Following it, we establish a novel spike detection algorithm based on the spiky feature of power spectral density (PSD) in detrended and normalized imaginary SWIR CrIS spectral radiances. The performance of the algorithm is assessed by applying it to the multiple years of the SNPP and NOAA-20 CrIS full spectral resolution (FSR) radiance data. Approximate 300 and 500 spike events per day are detected for SNPP and NOAA-20 CrIS individually. The spikes can obviously increase the uncertainty in brightness temperature measurements at SWIR window channels that is determined by hitting location of the interferogram arms and the impulse intensities. Strong impulse spikes can trigger spectral radiance uncertainty by several times in SWIR window channels. Even the weakest impulse event can increase spectral radiance uncertainty by 4% in SWIR window channels. In addition, our simulations and one case study of CrIS data show that the spikes can degrade the quality of the CrIS data at the SWIR band with the largest impact at absorbing channels and the smallest at the window channels. The resultant brightness temperature change are 3 K for absorbing channels and up to 1 K for window channels in the SWIR band, demonstrating the significance flagging the CrIS data that are contaminated by the moderate and strong spikes that are missed in the operational data processing.

Sensitivity of Multispectral Imager Liquid Water Cloud Microphysical Retrievals to the Index of Refraction

A cloud property retrieved from multispectral imagers having spectral channels in the shortwave infrared (SWIR) and/or midwave infrared (MWIR) is the cloud effective particle radius (CER), a radiatively relevant weighting of the cloud particle size distribution. The physical basis of the CER retrieval is the dependence of SWIR/MWIR cloud reflectance on the cloud particle single scattering albedo, which in turn depends on the complex index of refraction of bulk liquid water (or ice) in addition to the cloud particle size. There is a general consistency in the choice of the liquid water index of refraction by the cloud remote sensing community, largely due to the few available independent datasets and compilations. Here we examine the sensitivity of CER retrievals to the available laboratory index of refraction datasets in the SWIR and MWIR using the retrieval software package that produces NASA’s standard Moderate Resolution Imaging Spectroradiometer (MODIS)/Visible Infrared Imaging Radiometer suite (VIIRS) continuity cloud products. The sensitivity study incorporates two laboratory index of refraction datasets that include measurements at supercooled water temperatures, one in the SWIR and one in the MWIR. Neither has been broadly utilized in the cloud remote sensing community. It is shown that these two new datasets can significantly change CER retrievals (e.g., 1–2 µm) relative to common datasets used by the community. Further, index of refraction data for a 265 K water temperature gives more consistent retrievals between the two spectrally distinct 2.2 µm atmospheric window channels on MODIS and VIIRS. As a result, 265 K values from the SWIR and MWIR index of refraction datasets were adopted for use in the production version of the continuity cloud product. The results indicate the need to better understand temperature-dependent bulk water absorption and uncertainties in these spectral regions.

Inter-Calibration of AMSU-A Window Channels

More than one decade of observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard the polar-orbiting satellites NOAA-15 to NOAA-19 and European Meteorological Operational satellite program-A (MetOp-A) provided global information on atmospheric temperature profiles, water vapor, cloud, precipitation, etc. These observations were primarily intended for weather related prediction and applications, however, in order to meet the requirements for climate application, further reprocessing must be conducted to first eliminate any potential satellites biases. After the geolocation and cross-scan bias corrections were applied to the dataset, follow-on research focused on the comparison amongst AMSU-A window channels (e.g., 23.8, 31.4, 50.3 and 89.0 GHz) from the six different satellites to remove any inter-satellite inconsistency. Inter-satellite differences can arise from many error sources, such as bias drift, sun-heating-induced instrument variability in brightness temperatures, radiance dependent biases due to inaccurate calibration nonlinearity, etc. The Integrated microwave inter-calibration approach (IMICA) approach was adopted in this study for inter-satellite calibration of AMSU-A window channels after the appropriate standard deviation (STD) thresholds were identified to restrict Simultaneous Nadir Overpass (SNO) data for window channels. This was a critical step towards the development of a set of fundamental and thematic climate data records (CDRs) for hydrological and climatological applications. NOAA-15 served as the main reference satellite for this study. For ensuing studies that expand to beyond 2015, however, it is recommended that a different satellite be adopted as the reference due to concerns over potential degradation of NOAA-15 AMSU-A.

Improved Detection of Interannual Cloud Variability over the Southern Hemisphere Using Legacy Satellites

AbstractShifts in deep tropical convection and midlatitude jet streams both manifest themselves in high cloud anomalies. Such anomalies may play a significant role in local to global climate processes. This work investigates how high cloud properties covary with two primary interannual modes of variability in the Southern Hemisphere (SH): El Niño–Southern Oscillation (ENSO) and the southern annular mode (SAM). In contrast to several recent studies that utilize the latest remote sensing datasets (e.g., CloudSat), we employ a novel combination of imager and sounder data from legacy satellite instruments. Using these legacy data, we confirm the poleward shift of high cloud fields in the SH midlatitudes with SAM seen in other recent studies and characterize the opposing impacts of SAM and ENSO on the South Pacific convergence zone and Southern Hemisphere storm tracks. Furthermore, we demonstrate that the standard deviation of brightness temperature data from the window channel acts as a surrogate for high cloud fraction in the tropics and midlatitudes. Our results reconcile apparent differences in recent studies and suggest that brightness temperature standard deviations are climate relevant, in addition to being largely insensitive to instrument calibration.

Evaluating the Absolute Calibration Accuracy and Stability of AIRS Using the CMC SST

We compare the daily mean and standard deviation of the difference between the sea surface skin temperature (SST) derived from clear sky Atmospheric InfraRed Sounder (AIRS) data from seven atmospheric window channels between 2002 and 2020 and collocated Canadian Meteorological Centre (CMC) SST data from the tropical oceans. After correcting the mean difference for cloud contamination and diurnal effects, the remaining bias relative to the CMC SST, is reasonably consistent with estimates of the AIRS absolute accuracy based on the uncertainty of the pre-launch calibration. The time series of the bias produces trends well below the 10 mK/yr level required for climate change evaluations. The trends are in the 2 mK/yr range for the five window channels between 790 and 1231 cm−1, and +5 mK/yr for the shortwave channels. Between 2002 and 2020, the time series of the standard deviation of the difference between the AIRS SST and the CMC SST dropped fairly steadily to below 0.4 K in several AIRS window channels, a level previously only seen in gridded SST products relative to the Argo buoys.

Potential Impacts of Assimilating All-Sky Satellite Infrared Radiances on Convection-Permitting Analysis and Prediction of Tropical Convection

AbstractGeostationary infrared satellite observations are spatially dense [&gt;1/(20 km)2] and temporally frequent (&gt;1 h−1). These suggest the possibility of using these observations to constrain subsynoptic features over data-sparse regions, such as tropical oceans. In this study, the potential impacts of assimilating water vapor channel brightness temperature (WV-BT) observations from the geostationary Meteorological Satellite 7 (Meteosat-7) on tropical convection analysis and prediction were systematically examined through a series of ensemble data assimilation experiments. WV-BT observations were assimilated hourly into convection-permitting ensembles using Penn State’s ensemble square root filter (EnSRF). Comparisons against the independently observed Meteosat-7 window channel brightness temperature (Window-BT) show that the assimilation of WV-BT generally improved the intensities and locations of large-scale cloud patterns at spatial scales larger than 100 km. However, comparisons against independent soundings indicate that the EnSRF analysis produced a much stronger dry bias than the no data assimilation experiment. This strong dry bias is associated with the use of the simulated WV-BT from the prior mean during the EnSRF analysis step. A stochastic variant of the ensemble Kalman filter (NoMeanSF) is proposed. The NoMeanSF algorithm was able to assimilate the WV-BT without causing such a strong dry bias and the quality of the analyses’ horizontal cloud pattern is similar to EnSRF’s analyses. Finally, deterministic forecasts initiated from the NoMeanSF analyses possess better horizontal cloud patterns above 500 km than those of the EnSRF. These results suggest that it might be better to assimilate all-sky WV-BT through the NoMeanSF algorithm than the EnSRF algorithm.

Constant response window channel estimation method and LUT-based digital predistortion applied to OFDM VLC links

In this work, we investigate a LUT-based digital predistortion technique applied to a Visible Light Communication system. We study the performance of the predistortion technique for OFDM signals with up to 2048 subcarriers, 16-QAM modulation, and three levels of DC bias considering the voltage–current LED response of a commercially available device. In addition, we propose a new and simpler pilot-assisted approach for the OFDM channel estimation, named Constant Response Window, the performance being tested with up to 64-QAM modulation and 2048 IFFT size OFDM signals. BER and MSE results show that the combination of the LUT-based digital predistortion technique with the new estimation method presents an improved performance over the Least Squares and the Minimum Mean Square Error channel estimation techniques in the considered scenario.

Development and Testing of a Clear-Sky Data Selection Algorithm for FY-3C/D Microwave Temperature Sounder-2

The Fengyun (FY)-3C/D microwave temperature sounder-2 (MWTS-2) is similar to the Advanced Microwave Sounding Unit-A (AMSU-A), except it lacks two window channels located at 23.8 GHz and 31.4 GHz. This makes a clear-sky data determination challenging for the MWTS-2 due to the unavailability of cloud liquid water path (LWP) retrievable from the two window channels. The purpose of this study is to develop a clear-sky data selection algorithm for the FY-3C/D MWTS-2 based on the bias-removed differences between observations and model simulations of the MWTS-2 50.3-GHz channel 1 (or equivalently AMSU-A channel 3). First, a point is defined as a temporal clear-sky (cloudy) point if the bias-removed difference between observed and simulated brightness temperatures is smaller than or equal to (greater than) 2 K. Then, a temporal clear-sky (cloudy) point is defined as a final clear-sky (cloudy) point if all points within its 60-km (100-km) radial distance are temporal clear-sky (cloudy) points. Finally, if the mean value of the bias-removed differences between observations and simulations in the 100-km circle from a temporal cloudy point are smaller than or equal to (greater than) 2 K, all temporal clear-sky points within this circle are (not) taken as the final clear-sky points. Applications of this algorithm to FY-3C MWTS-2 and MetOp-B AMSU-A lead to the following conclusions: (i) more than 70% (95%) of the clear-sky (cloudy) data points are successfully identified from both AMSU-A and MWTS-2 observations; (ii) the algorithm-selected clear-sky data points were located in clear-sky areas in the GOES-15 imager, and (iii) the bias-removed differences between observations and model simulations of MWTS-2 channel 1 well reveals the eye, the eyewall, and the spiral rainband structure of Super Typhoon Halong (2014).

Typhoon Maria Precipitation Retrieval and Evolution Based on the Infrared Brightness Temperature of the Feng-Yun 4A/Advanced Geosynchronous Radiation Imager

Recognizing the importance and challenges inherent in the remote sensing of precipitation in typhoon monitoring, a study of the Advanced Geosynchronous Radiation Imager (AGRI) data from Feng-Yun 4A on typhoon precipitation was conducted. First, Typhoon Maria was selected to statistically analyze the AGRI infrared brightness temperature in the “precipitation” and “nonprecipitation” channels of the field of view. When there was precipitation, the brightness temperature of the AGRI channel changed significantly. Second, the shrunken locally linear embedding algorithm (SLLE) was adopted to carry out the retrieval of precipitation based on the brightness temperatures of AGRI infrared channels 8–14. The contribution rate of the brightness temperature at different channels to the objective function of precipitation retrieval model was obtained by the Bayesian model averaging (BMA). Based on the preliminary experimental “quantification” evaluation index, we concluded that the method adopted in this paper can be used to retrieve precipitation in infrared data and to retrieve the spiral cloud rain bands of a typhoon. Finally, based on the AGRI channel brightness temperature of a 10.8-micron window channel, we applied the membership degree information of a typhoon’s dominant cloud system from the fuzzy c-means (FCM) clustering method to modify precipitation retrieval results. The results were used to obtain the main morphological structure of typhoon precipitation. By further analyzing the temporal variation of dominant cloud system development using the FCM method, we concluded that the brightness temperature gradient can assist in the analysis of the variation of a typhoon’s intensity. This method can be applied to the continuous retrieval of large-scale precipitation. Precipitation retrieval via the AGRI can yield indicators for typhoon precipitation warnings and forecasts, thus providing a reliable reference tool for disaster prevention and mitigation.

Effect of channel temperature and mass window in the fission decay of Re*181

The dynamical cluster-decay model (DCM) with mass window restriction and channel temperature is applied to study the decay of hot and rotating compound nucleus $^{181}\mathrm{Re}^{*}$ formed through the reaction $^{12}\mathrm{C}+^{169}\mathrm{Tm}$ at three different experimental incident energies, 77.18, 83.22, and 89.25 MeV, for spherical and deformed fragments. So far in DCM, the evaluation of various fission observables such as mass and charge distribution, decay cross section, kinetic energy, excitation energy, etc., were studied considering the entire mass range covering the limiting values of the mass asymmetry from 0 to 1, accounting for emission of light particles, complex intermediate mass fragments, as well as fission fragments. These emissions are considered as the dynamical collective mass motion of preformed clusters through the barrier. In this work, restriction of the mass window is considered in DCM as that of the experimental data for which cross section of the fission fragments is measured. The mass window restriction enhances the formation probability of the fragments. Further, instead of using a fixed temperature for all mass asymmetries (or channels), temperature tuning is attempted to see its effect on the outcome of the model. The obtained results of cross section values are found to compare reasonably well with the experimental data.