Estimating Ice Water Path in Tropical Cyclones With Multispectral Microwave Data From the FY-3B Satellite

Wang, Yu; Fu, Yunfei; Fang, Xiang; Zhang, Ying

doi:10.1109/tgrs.2013.2290320

Cited by 11 publications

(7 citation statements)

References 36 publications

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“…Moreover, based on the algorithm by Liu and Curry [40], the surface precipitation rate derived from MWRI was obtained to compare with the simulated surface precipitation rate. In addition, the simulated frozen precipitation hydrometeor (graupel and snow) path was compared to retrievals from the Microwave Humidity Sounder and MWRI [39]. Because raw brightness temperatures represent an integration of electromagnetic radiation information from emission and scattering by all of surface and atmospheric constituents, there is a considerable amount of ambiguity in directly interpreting raw brightness temperatures in terms of hydrometeor properties [17,25,41].…”

Section: Fy-3b Observationmentioning

confidence: 99%

Satellite-Based Assessment of Various Cloud Microphysics Schemes in Simulating Typhoon Hydrometeors

Zhang

Liu

et al. 2019

Advances in Meteorology

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The accurate simulation of typhoon hydrometeors remains a challenge. This study attempts to evaluate the performances of five microphysics schemes (MPSs) in the Weather Research and Forecasting (WRF) model in simulating the supertyphoon Neoguri in July 2014. The observed microwave brightness temperature, as well as retrieved data from the microwave radiometer imager (MWRI) onboard Chinese FY-3B satellite, are used to test hydrometeor simulations. In particular, two MWRI radiance indices, including the emission index (EI) and scattering index (SI), are used to assess the performance of five MPSs in simulating liquid and frozen hydrometeors, respectively. Overall, the WRF model can well reproduce the overall pattern of typhoon-produced precipitation, albeit with slightly overestimated precipitation in the inner rainband and underestimated precipitation in the stratiform rainband. Moreover, ice water paths (IWPs) from all five MPS simulations are higher than those estimated from MWRI retrieval in most areas, and the spatial pattern and values of IWP for the National Severe Storms Laboratory double-moment MPS (NSSL) are much closer to those for MWRI. The NSSL scheme reproduces a more realistic joint histogram distribution of SI and EI than other MPSs do, relative to the observation. Besides, the nonlinear Lucas–Kanade optical flow approach has been used to reflect the horizontal distribution of hydrometeors in the typhoon. The results show that the simulated EI and SI from the five MPSs show a systematic southwest bias of approximately about 10∼20 km and significant intensity bias in the convection area. Further model sensitivity tests confirm that the NSSL scheme generates more realistic graupel and supercooled water close to the observations among all MPSs. The findings suggest that satellite measurements would be helpful to assess MPSs in numeric weather models, especially for hydrometeor distributions in the whole typhoon system.

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Section: Fy-3b Observationmentioning

confidence: 99%

Satellite-Based Assessment of Various Cloud Microphysics Schemes in Simulating Typhoon Hydrometeors

Zhang

Liu

et al. 2019

Advances in Meteorology

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“…Depending on the wavelength of observation, satellite remote sensing can measure different cloud microphysics. Microwave measurements can penetrate deeper into cloud layers to measure thick and dense ice clouds, while infrared and visible instruments are mainly used for thin cloud measurements around cloud tops (Liu and Curry, 1998;Weng and Grody, 2000;Stubenrauch et al, 2013). Although the ice water path (IWP) obtained from different instruments shows several differences (Stephens and Kummerow, 2007;Wu et al, 2009), it is of great importance to use remote sensing to determine the bulk and microphysical properties of clouds.…”

Section: Introductionmentioning

confidence: 99%

“…The brightness temperature (TB) depression caused by the scattering of ice particles is usually proportional to the IWP, which simplifies the retrieval method from radiometric measurements (Liu and Curry, 2000). Studies on ice cloud retrieval using radiometers such as the Advanced Microwave Sounding Unit (AMSU), Special Sensor Microwave Imager/-Sounder (SSMIS), Microwave Humidity Sounder (MHS), and MicroWave Humidity Sounder (MWHS), as well as limb sounders such as the Microwave Limb Sounder (MLS), Sub-Millimetre Radiometer (SMR), and Superconducting Submillimeter-Wave Limb Emission Sounder (SMILES), have been published for years (Zhao and Weng, 2002;Eriksson et al, 2007;Wu et al, 2008;Sun and Weng, 2012;Millán et al, 2013;Wang et al, 2014). However, these spaceborne radiometers lack the ability to conduct polarization measurement, while dual-polarization measurements above 100 GHz show obvious polarized scattering signals of ice clouds.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of ice water path from the Microwave Humidity Sounder (MWHS) aboard FengYun-3B (FY-3B) satellite polarimetric measurements based on a deep neural network

Wang

et al. 2022

Atmos. Meas. Tech.

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Abstract. The ice water path (IWP) is an important cloud parameter in atmospheric radiation, and there are still great difficulties in its retrieval. Artificial neural networks have become a popular method in atmospheric remote sensing in recent years. This study presents a global IWP retrieval based on deep neural networks using the measurements from the Microwave Humidity Sounder (MWHS) aboard the FengYun-3B (FY-3B) satellite. Since FY-3B/MWHS has quasi-polarization channels at 150 GHz, the effect of the polarimetric radiance difference (PD) was also studied. A retrieval database was established using collocations between MWHS and CloudSat 2C-ICE (CloudSat and CALIPSO Ice Cloud Property Product). Then, two types of networks were trained for cloud scene filtering and IWP retrieval. For the cloud filtering network, the microwave channels show a capacity with a false alarm ratio (FAR) of 0.31 and a probability of detection (POD) of 0.61. For the IWP retrieval network, different combination inputs of auxiliaries and channels were compared. The results show that the five MWHS channels combined with scan angle, latitude, and the ocean/land mask of inputs of auxiliary variables perform best. Applying the cloud filtering network and IWP retrieval network, the final root mean squared error (RMSE) is 916.76 g m−2, the mean absolute percentage error (MAPE) is 92 %, and the correlation coefficient (CC) is 0.65. Then, a tropical cyclone case measured simultaneously by MWHS and CloudSat was chosen to test the performance of the networks, and the result shows a good correlation (0.73) with 2C-ICE. Finally, the global annual mean IWP of MWHS is very close to that of 2C-ICE, and the 150 GHz channels give a significant improvement in the midlatitudes compared to using only 183 GHz channels.

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“…The microwave remote sensing on satellites with the passive radiometers has the advantage over optical and infrared sensing that it can capture images both day and night and even through clouds [1,2]. Unique information, including the total water vapor content, the snow depth and the ozone profile, can also be provided in the microwave frequency band [3][4][5][6]. Due to the fact of the limited sampling interval and a series of degeneration factors, such as the antenna pattern, the receiver sensitivity and the sensors' scanning mode, the data obtained by satellite microwave radiometers has relatively poor spatial resolution [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

et al. 2019

Remote Sensing

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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.

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Estimating Ice Water Path in Tropical Cyclones With Multispectral Microwave Data From the FY-3B Satellite

Cited by 11 publications

References 36 publications

Satellite-Based Assessment of Various Cloud Microphysics Schemes in Simulating Typhoon Hydrometeors

Satellite-Based Assessment of Various Cloud Microphysics Schemes in Simulating Typhoon Hydrometeors

Retrieval of ice water path from the Microwave Humidity Sounder (MWHS) aboard FengYun-3B (FY-3B) satellite polarimetric measurements based on a deep neural network

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

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