A Quality Control Procedure for FY-3A MWTS Measurements with Emphasis on Cloud Detection Using VIRR Cloud Fraction

2013 ◽  
Vol 30 (8) ◽  
pp. 1704-1715 ◽  
Author(s):  
Juan Li ◽  
Xiaolei Zou
Keyword(s):  
Quality Control ◽  
Detection Algorithm ◽  
Cloud Fraction ◽  
Control Procedure ◽  
Test Results ◽  
Cloud Detection ◽  
Liquid Water Path ◽  
Model Simulations ◽  
Cloud Liquid Water Path ◽  
Environmental Prediction

Abstract A quality control (QC) procedure for satellite radiance assimilation is proposed and applied to radiance observations from the Microwave Temperature Sounder (MWTS) on board the first satellite of the Chinese polar-orbiting Fengyun-3 series (FY-3A). A cloud detection algorithm is incorporated based on the cloud fraction product provided by the Visible and Infrared Radiometer (VIRR) on board FY-3A. Analysis of the test results conducted in July 2011 indicates that most clouds are identifiable by applying an FY-3A VIRR cloud fraction threshold of 37%. This result is verified with the cloud liquid water path data from the Meteorological Operational Satellite A (MetOp-A). On average, 56.1% of the global MWTS data are identified as cloudy by the VIRR-based cloud detection method. Other QC steps include the following: (i) two outmost field of views (FOVs), (ii) use of channel 3 if the terrain altitude is greater than 500 m, (iii) channel 2 over sea ice and land, (iv) coastal FOVs, and (v) outliers with large differences between model simulations and observations. About 82%, 74%, and 29% of the MWTS observations are removed by the proposed QC for channels 2–4, respectively. An approximate 0.5-K scan bias improvement is achieved with QC, with a large impact at edges of the field of regard for channels 2–4. After QC, FY-3A MWTS global data more closely resemble the National Centers for Environmental Prediction (NCEP) forecast data, the global biases and standard deviations are reduced significantly, and the frequency distribution of the differences between observations and model simulations become more Gaussian.

scholarly journals A quality control procedure for Fengyun-3A microwave temperature sounder with emphasis on a new cloud detection algorithm

2016 ◽  
Vol 66 (1) ◽  
pp. 19
Author(s):  
Xiang Wang ◽  
Yifang Ren ◽  
Gang Li
Keyword(s):  
Quality Control ◽  
Liquid Water ◽  
Detection Algorithm ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Control Procedure ◽  
Cloud Detection ◽  
Liquid Water Path ◽  
Model Simulations ◽  
Water Path

A cloud detection algorithm for satellite radiance from the microwave temperature sounder (MWTS) on board the first satellite of the Chinese polar-orbiting Fengyun-three series (FY-3A) is proposed based on the measurements at the frequencies of 50.3 and 53.6 GHz. The cloud liquid water path index (LWP index) is calculated using the brightness temperature at these two channels. Analysis of one case carried out in January2010shows the great consistency be-tween this new algorithm result and the available liquid water path product from the Meteorological Operational satellite A (MetOp-A).In general, about 60% of the global MWTS data are considered to be contaminated by cloud by virtue of the new cloud detection algorithm. A quality control (QC) procedure is applied to MWTS measurements with emphasis on the cloud detection. The QC steps are composed of (i) channel 2 over sea ice, land and coastal field of views (FOVs); (ii) channels 2 and 3 over cloudy FOVs; and (iii) outliers with large differences between observations and model simulations. After QC, MWTS measurements of channels 2–4 agree very well with the model simulations using the National Centres for Environmental Prediction (NCEP) forecast data as radiative transfer model input; the scan biases are reduced significantly, especially at the edges of the swath; and the frequency distributions of the differences between observations and model simulations become more Gaussian-like.

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

2020 ◽  
Vol 12 (9) ◽  
pp. 1478 ◽  
Author(s):  
Zeyi Niu ◽  
Xiaolei Zou ◽  
Peter Sawin Ray
Keyword(s):  
Radial Distance ◽  
Mean Value ◽  
Data Selection ◽  
Selection Algorithm ◽  
Liquid Water Path ◽  
Model Simulations ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Data Points ◽  
Cloud Liquid Water Path

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).

Assessment of the Concrete Compressive Strength in Existing Structures Based on Core Test Results

2018 ◽  
Vol 272 ◽  
pp. 238-243 ◽  
Author(s):  
Viktar V. Tur ◽  
Stanislav S. Derechennik
Keyword(s):  
Monte Carlo Simulation ◽  
Quality Control ◽  
Compressive Strength ◽  
Structural Reliability ◽  
Reliability Estimation ◽  
Control Procedure ◽  
Test Results ◽  
Concrete Compressive Strength ◽  
Existing Structures

Evaluation of the concrete compressive strength in existing structures is an important problem, which is associated with structural reliability estimation as well as a quality control procedure. In accordance with a new concept of EN 13791, reported by T.A.Harrison, one of the main targets of the standard is to determine not a class, but in-situ characteristic concrete compressive strength. Hereby proposed criterion for the estimation of the in-situ characteristic concrete compressive strength is based on the non-parametric confidence interval for quantile. This criterion was verified by the both Monte Carlo simulation and test results under the real concrete structures.

Impact of Holuhraun volcano aerosol on clouds in cloud-system resolving simulations

2021 ◽  
Author(s):  
Mahnoosh Haghighatnasab ◽  
Johannes Quass
Keyword(s):  
Liquid Water ◽  
Cloud Droplet ◽  
Volcanic Eruptions ◽  
Cloud Fraction ◽  
Number Concentration ◽  
Liquid Water Path ◽  
Water Path ◽  
Cloud Droplet Number Concentration ◽  
Cloud Liquid Water Path ◽  
Cloud Response

<p>Since increased anthropogenic aerosol result in an enhancement in cloud droplet number concentration, cloud and precipitation process are modified. It is unclear how exactly cloud liquid water path (LWP) and cloud fraction respond to aerosol perturbations. A large volcanic eruption may help to better understand and quantify the cloud response to external perturbations, with a focus on the short-term cloud adjustments . Volcloud is one of the research projects in the Vollmpact collaborative German research unit which aims to the improve understanding of how the climate system responds to volcanic eruptions. This includes skills in satellite remote sensing of atmospheric composition, stratospheric aerosol parameters and clouds as well as in modelling of aerosol microphysical and cloud processes, and in climate modelling. The goal of VolCloud is to understand and quantify the response of clouds to volcanic eruptions and to thereby advance the fundamental understanding of the cloud response to external forcing, particularly aerosol-cloud interactions. In this study we used ICON-NWP atmospheric model at a cloud-system-resolving resolution of 2.5 km horizontally, to simulate the region around the Holuhraun volcano for the duration of one week (1 – 7 September 2014). The pair of simulations, with and without the volcanic aerosol emissions allowed us to assess the simulated effective radiative forcing and its mechanisms as well as its impact on adjustments of cloud liquid water path and cloud fraction to the perturbations of cloud droplet number concentration. In this case studies liquid water path positively correlates with enhanced cloud droplet concentration.</p>

scholarly journals Improved Quantitative Precipitation Forecasts by MHS Radiance Data Assimilation with a Newly Added Cloud Detection Algorithm

2013 ◽  
Vol 141 (9) ◽  
pp. 3203-3221 ◽  
Author(s):  
Xiaolei Zou ◽  
Zhengkun Qin ◽  
Fuzhong Weng
Keyword(s):  
Quality Control ◽  
Data Assimilation ◽  
Control Algorithm ◽  
Control Process ◽  
Detection Algorithm ◽  
Precipitation Forecast ◽  
Precipitation Event ◽  
Cloud Detection ◽  
Quality Control Process ◽  
Radiance Data

Abstract Satellite microwave humidity sounding data are assimilated through the gridpoint statistical interpolation (GSI) analysis system into the Advanced Research core of the Weather Research and Forecasting (WRF) model (ARW) for a coastal precipitation event. A detailed analysis shows that uses of Microwave Humidity Sounder (MHS) data from both NOAA-18 and MetOp-A results in GSI degraded precipitation threat scores in a 24-h model forecast. The root cause for this degradation is related to the MHS quality control algorithm, which is supposed to remove cloudy radiances. Currently, the GSI cloud detection is based on the brightness temperature differences between observations and the model background state at two MHS window channels. It is found that the GSI quality control algorithm fails to identify some MHS cloudy radiances in cloud edges where the ARW model has no cloud and the water vapor amount is low. A new MHS cloud detection algorithm is developed based on a statistical relationship between three MHS channels and the Geostationary Operational Environmental Satellite (GOES) imager channel at 10.7 μm. The 24-h quantitative precipitation forecast is improved rather than degraded by MHS radiance data assimilation when the new cloud detection algorithm is added to the GSI MHS quality control process. The temporal evolution of 3-h accumulative rainfall distributions compared favorably with that of multisensor NCEP observations and GOES-12 imager observations. The precipitation threat scores are increased by more than 50% after 3–6 h of model forecasts for 3-h rainfall thresholds exceeding 1.0 mm.

scholarly journals Operational generation of AVHRR-based cloud products for Europe and the Arctic at EUMETSAT's Satellite Application Facility on Climate Monitoring (CM-SAF)

2009 ◽  
Vol 3 (1) ◽  
pp. 45-51 ◽  
Author(s):  
F. Kaspar ◽  
R. Hollmann ◽  
M. Lockhoff ◽  
K.-G. Karlsson ◽  
A. Dybbroe ◽  
...  
Keyword(s):  
Cloud Fraction ◽  
The Arctic ◽  
Monthly Means ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Cloud Parameters ◽  
Climate Monitoring ◽  
Cloud Optical Thickness ◽  
Cloud Liquid Water Path ◽  
New Processing

Abstract. The Satelite Application Facility on Climate Monitoring has implemented a new processing environment for AVHRR-based climate monitoring products. AVHRR measurements from NOAA-17, NOAA-18 and MetOp-A are utilized to generate daily and monthly means of several cloud parameters for Europe and the Inner Arctic: Cloud fraction, cloud types, cloud phase, cloud top height, cloud optical thickness and cloud liquid water path.

scholarly journals Satellite-based estimate of the variability of warm cloud properties associated with aerosol and meteorological conditions

2018 ◽  
Author(s):  
Yuqin Liu ◽  
Jiahua Zhang ◽  
Putian Zhou ◽  
Tao Lin ◽  
Juan Hong ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Cloud Droplet ◽  
Cloud Fraction ◽  
Meteorological Conditions ◽  
Atmospheric Conditions ◽  
Liquid Water Path ◽  
Aerosol Cloud ◽  
Cloud Properties ◽  
Low Cloud ◽  
Cloud Liquid Water Path

Abstract. Aerosol-cloud interaction is examined using four years of data from the MODIS/Terra (morning orbit) and MODIS/Aqua (afternoon orbit) satellites. Aerosol optical depth (AOD) and cloud properties retrieved from both sensors are used to explore in a statistical sense the morning-to-afternoon variation of cloud properties in conditions with low and high AOD, over both land and ocean. The results show that the morning-to-afternoon variation of cloud properties during the 3 hours between the Terra and Aqua overpasses have similar patterns (increase or decrease) over land under both low and high AOD conditions. The variation in d(Cloud_X), defined as the mean change in cloud property Cloud_X between the morning and afternoon overpasses in high AOD conditions minus that in low AOD conditions, is different over land and ocean. This applies to cloud droplet effective radius (CDR), cloud fraction (CF) and cloud top pressure (CTP), but not to cloud optical thickness (COT) and cloud liquid water path (CWP). The effects of initial cloud fraction and meteorological conditions on the change in CF are also explored, showing that upward motion of air parcels can enhance the cloud cover much more when AOD is high than when it is low. In contrast, the increase of cloud cover with increasing relative humidity is much stronger in a relatively clean atmosphere with low AOD than in a more polluted atmosphere. Meanwhile, stable atmospheric conditions favour the development of a low cloud cover, especially when AOD is high. Overall, the analysis of the diurnal variation of cloud properties provides a better understanding of aerosol-cloud interaction over land and ocean.

scholarly journals Cloud fraction determined by thermal infrared and visible all-sky cameras

2018 ◽  
Author(s):  
Christine Aebi ◽  
Julian Gröbner ◽  
Niklaus Kämpfer
Keyword(s):  
Cloud Amount ◽  
Visible Spectrum ◽  
Thermal Infrared ◽  
Detection Algorithm ◽  
Cloud Fraction ◽  
High Temporal Resolution ◽  
Cloud Detection ◽  
Fractional Cloud ◽  
Significant Difference ◽  
High Level

Abstract. The thermal infrared cloud camera (IRCCAM) is a prototype instrument that determines cloud fraction continuously during day and nighttime with high temporal resolution. It has been developed and tested at Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center (PMOD/WRC) in Davos, Switzerland. The IRCCAM consists of a commercial microbolometer camera sensitive in the 8 μm–14 μm wavelength range. Over a time period of two years, the fractional cloud coverage obtained by the IRCCAM is compared with two other commercial cameras sensitive in the visible spectrum (Mobotix Q24M and Schreder VIS-J1006) as well as with the automated partial cloud amount detection algorithm (APCADA) using pyrgeometer data. In comparison to the visible cloud detection algorithms, the IRCCAM shows median difference values of 0.01 to 0.07 cloud fraction wherein around 90 % of the data are within ±0.25 (±2 oktas) cloud fraction. Thus there is no significant difference in the cloud fraction determination of the IRCCAM in comparison to the other study instruments. Analysis indicates no significant difference in the performance of the IRCCAM during day or nighttime and also not in different seasons. The cloud types where all algorithms are in closest agreement are low-level clouds (with median differences in cloud fraction of −0.01 to 0.02), followed by mid-level (0.00) and high-level clouds (−0.13).

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