PATMOS-x v6.0: Improvements to AVHRR Cloud Climate Record and Analysis of the Updated Data

2020 ◽  
Author(s):  
Coda Phillips ◽  
Michael Foster ◽  
Andrew Heidinger
Keyword(s):  
High Resolution ◽  
Detection Accuracy ◽  
Cloud Detection ◽  
Edge Preserving ◽  
Brightness Temperatures ◽  
Cloud Properties ◽  
Satellite Sensors ◽  
The Difference ◽  
Climate Analysis ◽  
Spectral Channels

<p>Since 1978, an Advanced Very-High-Resolution Radiometer (AVHRR) has flown onboard 17 polar-orbiting satellites. Together, they are the longest global record from a homogeneous set of satellite sensors. The Pathfinder Atmosphere’s Extended (PATMOS-x) dataset is a long-term cloud record derived from the AVHRR radiances, and suitable for climate analysis. It has demonstrated intersensor stability and has been rigorously compared with other cloud datasets.</p><p>However, the AVHRR alone has only limited spectral information, so cloud detection during nighttime or over ice is challenging. Therefore, performance degrades over regions with extreme diurnal patterns or low temperatures such as the poles, despite our interest.</p><p>The next production version of PATMOS-x will include numerous algorithmic changes as well as the use of High-resolution Infrared Radiation Sounder (HIRS) spectral channels to improve detection accuracy in previously difficult conditions. The low-resolution HIRS soundings are upsampled to match the AVHRR pixels through an edge-preserving process called “fusion”. The higher-resolution AVHRR imagery guides the upsampling and the resulting combination is spectrally consistent with the AVHRR and has a high spatial resolution.</p><p>For cloud detection, the difference between the AVHRR and HIRS 11μm and HIRS 6.7μm brightness temperatures has been added as a feature in the naive Bayesian cloud detector. The effect on cloud precision is seen especially in the Antarctic where false-positive cloud detections have decreased dramatically.</p><p>Other cloud properties can be improved with the new spectral channels. For example, the new cloud phase algorithm uses the HIRS 6.7μm to determine cloud phase and the AVHRR and HIRS 11μm-13.3μm beta ratio identifies overlapping clouds. Also, the 11μm, 12μm, and HIRS 13.3μm are used in the new cloud height algorithm.</p><p>We report on the development of this new version of the PATMOS-x cloud climate dataset, and the methods used to calibrate and homogenize the participating sensors. Finally, observed trends in the improved dataset will be examined and related to the old dataset. In particular, attention will be given to whether high-latitude analysis of climatic trends is finally possible on the new dataset.</p>

scholarly journals Aggregate-area radiative flux biases

2002 ◽  
Vol 34 ◽  
pp. 101-105 ◽  
Author(s):  
Xuanji Wang ◽  
Jeffrey R. Key
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Heat Budget ◽  
Numerical Models ◽  
Radiative Flux ◽  
The Arctic ◽  
Radiative Fluxes ◽  
Cloud Properties ◽  
Model Cell ◽  
The Difference

AbstractMost climate models treat surface and atmospheric properties as being horizontally homogeneous and compute surface radiative fluxes with average gridcell properties. In this study it is found that large biases can occur if sub-gridcell variability is ignored, where bias is defined as the difference between the average of fluxes computed at high resolution within a model cell and the flux computed with the average surface and cloud properties within the cell. Data from the Advanced Very High Resolution Radiometer for the year-long Surface Heat Budget of the Arctic Ocean (SHEBA) experiment are used to determine biases in aggregate-area fluxes. A simple regression approach to correct for biases that result from horizontal variability was found to reduce the average radiative flux bias to near zero. The correction can be easily implemented in numerical models.

scholarly journals A robust threshold-based cloud mask for the HRV channel of MSG SEVIRI

2013 ◽  
Vol 6 (2) ◽  
pp. 2829-2855
Author(s):  
S. Bley ◽  
H. Deneke
Keyword(s):  
High Resolution ◽  
Cloud Fraction ◽  
Small Scale ◽  
Cloud Detection ◽  
Low Resolution ◽  
Test Dataset ◽  
Clear Sky ◽  
Cloud Mask ◽  
Spectral Channels ◽  
Selection Of

Abstract. A robust threshold-based cloud mask for the high-resolution visible (HRV) channel (1 × 1 km2) of the METEOSAT SEVIRI 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 which 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 behaviour 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 dataset 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 estimate of cloud fraction.

scholarly journals Long-Term and High-Resolution Global Time Series of Brightness Temperature from Copula-Based Fusion of SMAP Enhanced and SMOS Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1842 ◽  
Author(s):  
Christof Lorenz ◽  
Carsten Montzka ◽  
Thomas Jagdhuber ◽  
Patrick Laux ◽  
Harald Kunstmann
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
High Resolution ◽  
Brightness Temperature ◽  
Spatial Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Brightness Temperatures ◽  
Climate Analysis

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture.

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.

Low Cloud Detection in Multilayer Scenes using Satellite Imagery with Machine Learning Methods

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Infrared Imaging ◽  
Weather Forecasting ◽  
Auxiliary Information ◽  
Probability Of Detection ◽  
Cloud Detection ◽  
Brightness Temperatures ◽  
Low Clouds ◽  
Low Cloud ◽  
Satellite Sensors

Abstract The detection of multilayer clouds in the atmosphere can be particularly challenging from passive visible and infrared imaging radiometers since cloud boundary information is limited primarily to the topmost cloud layer. Yet detection of low clouds in the atmosphere is important for a number of applications, including aviation nowcasting and general weather forecasting. In this work, we develop pixel-based machine learning-based methods of detecting low clouds, with a focus on improving detection in multilayer cloud situations and specific attention given to improving the Cloud Cover Layers (CCL) product, which assigns cloudiness in a scene into vertical bins. The Random Forest (RF) and Neural Network (NN) implementations use inputs from a variety of sources, including GOES Advanced Baseline Imager (ABI) visible radiances, infrared brightness temperatures, auxiliary information about the underlying surface, and relative humidity (which holds some utility as a cloud proxy). Training and independent validation enlists near-global, actively-sensed cloud boundaries from the radar and lidar systems onboard the CloudSat and CALIPSO satellites. We find that the RF and NN models have similar performances. The probability of detection (PoD) of low cloud increases from 0.685 to 0.815 when using the RF technique instead of the CCL methodology, while the false alarm ratio decreases. The improved PoD of low cloud is particularly notable for scenes that appear to be cirrus from an ABI perspective, increasing from 0.183 to 0.686. Various extensions of the model are discussed, including a nighttime-only algorithm and expansion to other satellite sensors.

scholarly journals Subpixel Characterization of HIRS Spectral Radiances Using Cloud Properties from AVHRR

2016 ◽  
Vol 33 (7) ◽  
pp. 1519-1538 ◽  
Author(s):  
Paul W. Staten ◽  
Brian H. Kahn ◽  
Mathias M. Schreier ◽  
Andrew K. Heidinger
Keyword(s):  
High Resolution ◽  
Global Scale ◽  
Small Scale ◽  
Spectral Brightness ◽  
Cloud Type ◽  
Brightness Temperatures ◽  
Cloud Properties ◽  
Ocean Region ◽  
Intersatellite Calibration

AbstractThis paper describes a cloud type radiance record derived from NOAA polar-orbiting weather satellites using cloud properties retrieved from the Advanced Very High Resolution Radiometer (AVHRR) and spectral brightness temperatures (Tb) observed by the High Resolution Infrared Radiation Sounder (HIRS). The authors seek to produce a seamless, global-scale, long-term record of cloud type and Tb statistics intended to better characterize clouds from seasonal to decadal time scales. Herein, the methodology is described in which the cloud type statistics retrieved from AVHRR are interpolated onto each HIRS footprint using two cloud classification methods. This approach is tested over the northeast tropical and subtropical Pacific Ocean region, which contains a wide variety of cloud types during a significant ENSO variation from 2008 to 2009. It is shown that the Tb histograms sorted by cloud type are realistic for all HIRS channels. The magnitude of Tb biases among spatially coincident satellite intersections over the northeast Pacific is a function of cloud type and wavelength. While the sign of the bias can change, the magnitudes are generally small for NOAA-18 and NOAA-19, and NOAA-19 and MetOp-A intersections. The authors further show that the differences between calculated standard deviations of cloud-typed Tb well exceed intersatellite calibration uncertainties. The authors argue that consideration of higher-order statistical moments determined from spectral infrared observations may serve as a useful long-term measure of small-scale spatial changes, in particular cloud types over the HIRS–AVHRR observing record.

scholarly journals Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset version 3: 35-year climatology of global cloud and radiation properties

2020 ◽  
Vol 12 (1) ◽  
pp. 41-60 ◽  
Author(s):  
Martin Stengel ◽  
Stefan Stapelberg ◽  
Oliver Sus ◽  
Stephan Finkensieper ◽  
Benjamin Würzler ◽  
...  
Keyword(s):  
High Resolution ◽  
Radiant Energy ◽  
Energy System ◽  
Global Scale ◽  
Radiative Flux ◽  
Surface Radiation ◽  
Digital Object Identifier ◽  
Cloud Detection ◽  
Cloud Properties ◽  
Very High

Abstract. We present version 3 of the Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset, which contains a comprehensive set of cloud and radiative flux properties on a global scale covering the period of 1982 to 2016. The properties were retrieved from AVHRR measurements recorded by the afternoon (post meridiem – PM) satellites of the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellite (POES) missions. The cloud properties in version 3 are of improved quality compared with the precursor dataset version 2, providing better global quality scores for cloud detection, cloud phase and ice water path based on validation results against A-Train sensors. Furthermore, the parameter set was extended by a suite of broadband radiative flux properties. They were calculated by combining the retrieved cloud properties with thermodynamic profiles from reanalysis and surface properties. The flux properties comprise upwelling and downwelling and shortwave and longwave broadband fluxes at the surface (bottom of atmosphere – BOA) and top of atmosphere (TOA). All fluxes were determined at the AVHRR pixel level for all-sky and clear-sky conditions, which will particularly facilitate the assessment of the cloud radiative effect at the BOA and TOA in future studies. Validation of the BOA downwelling fluxes against the Baseline Surface Radiation Network (BSRN) shows a very good agreement. This is supported by comparisons of multi-annual mean maps with NASA's Clouds and the Earth's Radiant Energy System (CERES) products for all fluxes at the BOA and TOA. The Cloud_cci AVHRR-PM version 3 (Cloud_cci AVHRR-PMv3) dataset allows for a large variety of climate applications that build on cloud properties, radiative flux properties and/or the link between them. For the presented Cloud_cci AVHRR-PMv3 dataset a digital object identifier has been issued: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003 (Stengel et al., 2019).

scholarly journals Aerosol midlatitude cyclone indirect effects in observations and high-resolution simulations

2018 ◽  
Vol 18 (8) ◽  
pp. 5821-5846 ◽  
Author(s):  
Daniel T. McCoy ◽  
Paul R. Field ◽  
Anja Schmidt ◽  
Daniel P. Grosvenor ◽  
Frida A.-M. Bender ◽  
...  
Keyword(s):  
High Resolution ◽  
Rain Rate ◽  
Cloud Droplet ◽  
Moisture Flux ◽  
Conveyor Belt ◽  
Liquid Water Path ◽  
Aerosol Cloud ◽  
Cloud Properties ◽  
The Difference ◽  
Aerosol Cloud Interactions

Abstract. Aerosol–cloud interactions are a major source of uncertainty in inferring the climate sensitivity from the observational record of temperature. The adjustment of clouds to aerosol is a poorly constrained aspect of these aerosol–cloud interactions. Here, we examine the response of midlatitude cyclone cloud properties to a change in cloud droplet number concentration (CDNC). Idealized experiments in high-resolution, convection-permitting global aquaplanet simulations with constant CDNC are compared to 13 years of remote-sensing observations. Observations and idealized aquaplanet simulations agree that increased warm conveyor belt (WCB) moisture flux into cyclones is consistent with higher cyclone liquid water path (CLWP). When CDNC is increased a larger LWP is needed to give the same rain rate. The LWP adjusts to allow the rain rate to be equal to the moisture flux into the cyclone along the WCB. This results in an increased CLWP for higher CDNC at a fixed WCB moisture flux in both observations and simulations. If observed cyclones in the top and bottom tercile of CDNC are contrasted it is found that they have not only higher CLWP but also cloud cover and albedo. The difference in cyclone albedo between the cyclones in the top and bottom third of CDNC is observed by CERES to be between 0.018 and 0.032, which is consistent with a 4.6–8.3 Wm−2 in-cyclone enhancement in upwelling shortwave when scaled by annual-mean insolation. Based on a regression model to observed cyclone properties, roughly 60 % of the observed variability in CLWP can be explained by CDNC and WCB moisture flux.

Origin of the alternate bright/dark contrast in HREM images of hexagonal crystals, particularly 6H-SiC

1993 ◽  
Vol 51 ◽  
pp. 914-915
Author(s):  
J.S. Bow ◽  
R.W. Carpenter ◽  
M.J. Kim
Keyword(s):  
High Resolution ◽  
Incident Beam ◽  
Hexagonal Crystal ◽  
Detailed Explanation ◽  
Similar Character ◽  
High Resolution Images ◽  
The Difference ◽  
Zigzag Shape ◽  
Hexagonal Crystals ◽  
Dark Contrast

A prominent characteristic of high-resolution images of 6H-SiC viewed from [110] is a zigzag shape with a period of 6 layers as shown in Fig.1. Sometimes the contrast is same through the 6 layers of (0006) planes (Fig.1a), but in most cases it appears as in Fig.1b -- alternate bright/dark contrast among every three (0006) planes. Alternate bright/dark contrast is most common for the thicker specimens. The SAD patterns of these two types of image are almost same, and there is no indication that the difference results from compositional ordering. O’Keefe et al. concluded this type of alternate contrast was due to crystal tilt in thick parts of the specimen. However, no detailed explanation was given. Images of similar character from Ti3Al, which is also a hexagonal crystal, were reported by Howe et al. Howe attributed the bright/dark contrast among alternate (0002) Ti3Al planes to phase shifts produced by incident beam tilt.

Sign in / Sign up

Export Citation Format

Share Document