scholarly journals A Land and Ocean Microwave Cloud Classification Algorithm Derived from AMSU-A and -B, Trained Using MSG-SEVIRI Infrared and Visible Observations

2011 ◽  
Vol 139 (8) ◽  
pp. 2347-2366 ◽  
Author(s):  
Filipe Aires ◽  
Francis Marquisseau ◽  
Catherine Prigent ◽  
Geneviève Sèze
Keyword(s):  
Classification Algorithm ◽  
Satellite Observations ◽  
Significant Information ◽  
Clear Sky ◽  
Cloud Classification ◽  
Infrared Imager ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Cloud Mask ◽  
High Clouds

AbstractA statistical cloud classification and cloud mask algorithm is developed based on Advanced Microwave Sounding Unit (AMSU-A and -B) microwave (MW) observations. The visible and infrared data from the Meteosat Third Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) are used to train the microwave classifier. The goal of the MW algorithms is not to fully reproduce this MSG-SEVIRI cloud classification, as the MW observations do not have enough information on clouds to reach this level of precision. The objective is instead to obtain a stand-alone MW cloud mask and classification algorithm that can be used efficiently in forthcoming retrieval schemes of surface or atmospheric parameters from microwave satellite observations. This is an important tool over both ocean and land since the assimilation of the MW observations in the operational centers is independent from the other satellite observations.Clear sky and low, medium, and opaque–high clouds can be retrieved over ocean and land at a confidence level of more than 80%. An information content analysis shows that AMSU-B provides significant information over both land and ocean, especially for the classification of medium and high clouds, whereas AMSU-A is more efficient over ocean when discriminating clear situations and low clouds.

scholarly journals Using MetOp-A AVHRR Clear-Sky Measurements to Cloud-Clear MetOp-A IASI Column Radiances

2011 ◽  
Vol 28 (9) ◽  
pp. 1104-1116 ◽  
Author(s):  
Eric S. Maddy ◽  
Thomas S. King ◽  
Haibing Sun ◽  
Walter W. Wolf ◽  
Christopher D. Barnet ◽  
...  
Keyword(s):  
High Spatial Resolution ◽  
Zero Bias ◽  
Clear Sky ◽  
Microwave Sounding Unit ◽  
Atmospheric Sounding ◽  
Microwave Sounding ◽  
Ecmwf Model ◽  
Skin Temperatures ◽  
Very High ◽  
Better Than

Abstract High spatial resolution measurements from the Advanced Very High Resolution Radiometer (AVHRR) on the Meteorological Operation (MetOp)-A satellite that are collocated to the footprints from the Infrared Atmospheric Sounding Interferometer (IASI) on the satellite are exploited to improve and quality control cloud-cleared radiances obtained from the IASI. For a partial set of mostly ocean MetOp-A orbits collected on 3 October 2010 for latitudes between 70°S and 75°N, these cloud-cleared radiances and clear-sky subpixel AVHRR measurements within the IASI footprint agree to better than 0.25-K root-mean-squared difference for AVHRR window channels with almost zero bias. For the same dataset, surface skin temperatures retrieved using the combined AVHRR, IASI, and Advanced Microwave Sounding Unit (AMSU) cloud-clearing algorithm match well with ECMWF model surface skin temperatures over ocean, yielding total uncertainties ≤1.2 K for scenes with up to 97% cloudiness.

scholarly journals Distinguishing Aerosols from Clouds in Global, Multispectral Satellite Data with Automated Cloud Classification Algorithms

2008 ◽  
Vol 25 (4) ◽  
pp. 501-518 ◽  
Author(s):  
Keith D. Hutchison ◽  
Barbara D. Iisager ◽  
Thomas J. Kopp ◽  
John M. Jackson
Keyword(s):  
Satellite System ◽  
Classification Algorithms ◽  
Cloud Classification ◽  
Infrared Imager ◽  
Other Information ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask ◽  
New Procedures

Abstract A new approach is presented to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit differences in both spectral and textural signatures between clouds and aerosols to isolate pixels originally classified as cloudy by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask algorithm that in reality contains heavy aerosols. The procedures have been tested and found to accurately distinguish clouds from dust, smoke, volcanic ash, and industrial pollution over both land and ocean backgrounds in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This new methodology relies strongly upon data collected in the 0.412-μm bandpass, where smoke has a maximum reflectance in the VIIRS bands while dust simultaneously has a minimum reflectance. The procedures benefit from the VIIRS design, which is dual gain in this band, to avoid saturation in cloudy conditions. These new procedures also exploit other information available from the VIIRS cloud mask algorithm in addition to cloud confidence, including the phase of each cloudy pixel, which is critical to identify water clouds and restrict the use of spectral tests that would misclassify ice clouds as heavy aerosols. Comparisons between results from these new procedures, automated cloud analyses from VIIRS heritage algorithms, manually generated analyses, and MODIS imagery show the effectiveness of the new procedures and suggest that it is feasible to identify and distinguish between clouds and heavy aerosols in a single cloud mask algorithm.

scholarly journals Impact of Assimilating FY-3D MWTS-2 Upper Air Sounding Data on Forecasting Typhoon Lekima (2019)

2021 ◽  
Vol 13 (9) ◽  
pp. 1841
Author(s):  
Zeyi Niu ◽  
Lei Zhang ◽  
Peiming Dong ◽  
Fuzhong Weng ◽  
Wei Huang
Keyword(s):  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Forecast Performance ◽  
Clear Sky ◽  
Steering Flow ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
The Mean ◽  
Data Assimilation System ◽  
Statistical Interpolation

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.

scholarly journals On the Importance of Vaisala RS92 Radiosonde Humidity Corrections for a Better Agreement between Measured and Modeled Satellite Radiances

2012 ◽  
Vol 29 (2) ◽  
pp. 248-259 ◽  
Author(s):  
Ajil Kottayil ◽  
Stefan A. Buehler ◽  
Viju O. John ◽  
Larry M. Miloshevich ◽  
M. Milz ◽  
...  
Keyword(s):  
Bias Reduction ◽  
Satellite Measurements ◽  
Clear Sky ◽  
Empirical Correction ◽  
Microwave Sounding Unit ◽  
Upper Tropospheric Humidity ◽  
Microwave Sounding ◽  
Atmospheric Radiation Measurement ◽  
Atmospheric Radiative Transfer ◽  
Sky Conditions

Abstract A study has been carried out to assess the importance of radiosonde corrections in improving the agreement between satellite and radiosonde measurements of upper-tropospheric humidity. Infrared [High Resolution Infrared Radiation Sounder (HIRS)-12] and microwave [Advanced Microwave Sounding Unit (AMSU)-18] measurements from the NOAA-17 satellite were used for this purpose. The agreement was assessed by comparing the satellite measurements against simulated measurements using collocated radiosonde profiles of the Atmospheric Radiation Measurement (ARM) Program undertaken at tropical and midlatitude sites. The Atmospheric Radiative Transfer Simulator (ARTS) was used to simulate the satellite radiances. The comparisons have been done under clear-sky conditions, separately for daytime and nighttime soundings. Only Vaisala RS92 radiosonde sensors were used and an empirical correction (EC) was applied to the radiosonde measurements. The EC includes correction for mean calibration bias and for solar radiation error, and it removes radiosonde bias relative to three instruments of known accuracy. For the nighttime dataset, the EC significantly reduces the bias from 0.63 to −0.10 K in AMSU-18 and from 1.26 to 0.35 K in HIRS-12. The EC has an even greater impact on the daytime dataset with a bias reduction from 2.38 to 0.28 K in AMSU-18 and from 2.51 to 0.59 K in HIRS-12. The present study promises a more accurate approach in future radiosonde-based studies in the upper troposphere.

scholarly journals A threshold-based cloud mask for the high-resolution visible channel of Meteosat Second Generation SEVIRI

2013 ◽  
Vol 6 (10) ◽  
pp. 2713-2723 ◽  
Author(s):  
S. Bley ◽  
H. Deneke
Keyword(s):  
High Resolution ◽  
Small Scale ◽  
Cloud Detection ◽  
Low Resolution ◽  
Data Set ◽  
Clear Sky ◽  
Infrared Imager ◽  
Cloud Optical Thickness ◽  
Cloud Mask ◽  
Spectral Channels

Abstract. A threshold-based cloud mask for the high-resolution visible (HRV) channel (1 × 1 km2) of the Meteosat SEVIRI (Spinning Enhanced Visible and Infrared Imager) instrument is introduced and evaluated. It is based on operational EUMETSAT cloud mask for the low-resolution channels of SEVIRI (3 × 3 km2), which is used for the selection of suitable thresholds to ensure consistency with its results. The aim of using the HRV channel is to resolve small-scale cloud structures that cannot be detected by the low-resolution channels. We find that it is of advantage to apply thresholds relative to clear-sky reflectance composites, and to adapt the threshold regionally. Furthermore, the accuracy of the different spectral channels for thresholding and the suitability of the HRV channel are investigated for cloud detection. The case studies show different situations to demonstrate the behavior for various surface and cloud conditions. Overall, between 4 and 24% of cloudy low-resolution SEVIRI pixels are found to contain broken clouds in our test data set depending on considered region. Most of these broken pixels are classified as cloudy by EUMETSAT's cloud mask, which will likely result in an overestimate if the mask is used as an estimate of cloud fraction. The HRV cloud mask aims for small-scale convective sub-pixel clouds that are missed by the EUMETSAT cloud mask. The major limit of the HRV cloud mask is the minimum cloud optical thickness (COT) that can be detected. This threshold COT was found to be about 0.8 over ocean and 2 over land and is highly related to the albedo of the underlying surface.

scholarly journals Expansion of the All-Sky Radiance Assimilation to ATMS at NCEP

2019 ◽  
Vol 147 (7) ◽  
pp. 2603-2620 ◽  
Author(s):  
Yanqiu Zhu ◽  
George Gayno ◽  
R. James Purser ◽  
Xiujuan Su ◽  
Runhua Yang
Keyword(s):  
Quality Control ◽  
Advanced Technology ◽  
Observation Error ◽  
Clear Sky ◽  
Radiance Assimilation ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Control Procedures ◽  
Sky Radiance ◽  
Analysis System

Abstract Since the implementation of all-sky radiance assimilation of the Advanced Microwave Sounding Unit-A (AMSU-A) in the operational hybrid 4D ensemble–variational Global Forecast System at NCEP in 2016, the all-sky approach has been tested to expand to the radiances of Advanced Technology Microwave Sounder (ATMS) in the Gridpoint Statistical Interpolation analysis system (GSI). Following the all-sky framework implemented for the AMSU-A radiances, ATMS radiance assimilation adopts similar procedures in quality control, bias correction, and model of observation error. Efforts have been focused on special considerations that are necessary because of the unique features of the ATMS radiances and water vapor channels, including surface properties based on fields of view size and shape, and taking care of large departures from the first guess (OmF) along coastlines and radiances affected by strong scattering. More importantly, it is shown that this work makes microwave radiance OmFs become more consistent among different sensors, and provides indications of the deficiencies in quality control procedures of the original ATMS and Microwave Humidity Sounder (MHS) clear-sky radiance assimilation. While the generalized tracer effect is noticed, the overall impact on the forecast skill is neutral. This work is included in the upcoming operational implementation in 2019.

scholarly journals Advanced Dual-Satellite Method for Detection of Low Stratus and Fog near Japan at Dawn from FY-4A and Himawari-8

2021 ◽  
Vol 13 (5) ◽  
pp. 1042
Author(s):  
Jung-Hyun Yang ◽  
Jung-Moon Yoo ◽  
Yong-Sang Choi
Keyword(s):  
Satellite Observations ◽  
Viewing Angle ◽  
Clear Sky ◽  
Geostationary Satellites ◽  
Optimal Thresholds ◽  
Skill Scores ◽  
Class Probability ◽  
Normalized Difference Snow Index ◽  
High Skill ◽  
Very High

The detection of low stratus and fog (LSF) at dawn remains limited because of their optical features and weak solar radiation. LSF could be better identified by simultaneous observations of two geostationary satellites from different viewing angles. The present study developed an advanced dual-satellite method (DSM) using FY-4A and Himawari-8 for LSF detection at dawn in terms of probability indices. Optimal thresholds for identifying the LSF from the spectral tests in DSM were determined by the comparison with ground observations of fog and clear sky in/around Japan between April to November of 2018. Then the validation of these thresholds was carried out for the same months of 2019. The DSM essentially used two traditional single-satellite tests for daytime such as the 0.65-μm reflectance (R0.65), and the brightness temperature difference between 3.7 μm and 11 μm (BTD3.7-11); in addition to four more tests such as Himawari-8 R0.65 and BTD13.5-8.5, the dual-satellite stereoscopic difference in BTD3.7-11 (ΔBTD3.7-11), and that in the Normalized Difference Snow Index (ΔNDSI). The four were found to show very high skill scores (POD: 0.82 ± 0.04; FAR, 0.10 ± 0.04). The radiative transfer simulation supported optical characteristics of LSF in observations. The LSF probability indices (average POD: 0.83, FAR: 0.10) were constructed by a statistical combination of the four to derive the five-class probability values of LSF occurrence in a grid. The indices provided more details and useful results in LSF spatial distribution, compared to the single satellite observations (i.e., R0.65 and/or BTD3.7-11) of either LSF or no LSF. The present DSM could apply for remote sensing of environmental phenomena if the stereoscopic viewing angle between two satellites is appropriate.

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