Andi Walther

Assessment of Global Cloud Datasets from Satellites: Project and Database Initiated by the GEWEX Radiation Panel

Clouds cover about 70% of Earths surface and play a dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere over the entire globe and across the wide range of spatial and temporal scales that compose weather and climate variability. Satellite cloud data records now exceed more than 25 years; however, climate data records must be compiled from different satellite datasets and can exhibit systematic biases. Questions therefore arise as to the accuracy and limitations of the various sensors and retrieval methods. The Global Energy and Water Cycle Experiment (GEWEX) Cloud Assessment, initiated in 2005 by the GEWEX Radiation Panel (GEWEX Data and Assessment Panel since 2011), provides the first coordinated intercomparison of publicly available, standard global cloud products (gridded monthly statistics) retrieved from measurements of multispectral imagers (some with multiangle view and polarization capabilities), IR soun...

A Naive Bayesian Cloud-Detection Scheme Derived from CALIPSO and Applied within PATMOS-x

AbstractThe naive Bayesian methodology has been applied to the challenging problem of cloud detection with NOAA’s Advanced Very High Resolution Radiometer (AVHRR). An analysis of collocated NOAA-18/AVHRR and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations was used to automatically and globally derive the Bayesian classifiers. The resulting algorithm used six Bayesian classifiers computed separately for seven surface types. Relative to CALIPSO, the final results show a probability of correct detection of roughly 90% over water, deserts, and snow-free land; 82% over the Arctic; and below 80% over the Antarctic. This technique is applied within the NOAA Pathfinder Atmosphere’s Extended (PATMOS-x) climate dataset and the Clouds from AVHRR Extended (CLAVR-x) real-time product generation system. Comparisons of the PATMOS-x results with those from International Satellite Cloud Climatology Project (ISCCP) and Moder...

Illuminating the Capabilities of the Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band

Daytime measurements of reflected sunlight in the visible spectrum have been a staple of Earth-viewing radiometers since the advent of the environmental satellite platform. At night, these same optical-spectrum sensors have traditionally been limited to thermal infrared emission, which contains relatively poor information content for many important weather and climate parameters. These deficiencies have limited our ability to characterize the full diurnal behavior and processes of parameters relevant to improved monitoring, understanding and modeling of weather and climate processes. Visible-spectrum light information does exist during the nighttime hours, originating from a wide variety of sources, but its detection requires specialized technology. Such measurements have existed, in a limited way, on USA Department of Defense satellites, but the Suomi National Polar-orbiting Partnership (NPP) satellite, which carries a new Day/Night Band (DNB) radiometer, offers the first quantitative measurements of nocturnal visible and near-infrared light. Here, we demonstrate the expanded potential for nocturnal low-light visible applications enabled by the DNB. Via a combination of terrestrial and extraterrestrial light sources, such observations are always available—expanding many current existing applications while enabling entirely new capabilities. These novel low-light measurements open doors to a wealth of new interdisciplinary research topics while lighting a pathway toward the optimized design of follow-on satellite based low light visible sensors.

THE PATHFINDER ATMOSPHERES-EXTENDED AVHRR CLIMATE DATASET

The Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres–Extended (PATMOS-x) dataset offers over three decades of global observations from the NOAA Polar-orbiting Operational Environmental Satellite (POES) project and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) [Meteorological Operational (MetOp)] satellite series. The AVHRR has flown since 1978 and continues to provide radiometrically consistent observations with a spatial resolution of roughly 4 km and a temporal resolution of an ascending and descending node per satellite per day, achieving global coverage. The AVHRR PATMOS-x data provide calibrated AVHRR observations in addition to properties about tropospheric clouds and aerosols, Earths surface, Earths radiation budget, and relevant ancillary data. To provide three decades of data in a convenient format, PATMOS-x generates mapped and sampled results with a spatial resolution of 0.1° on a global latitude–longitude grid. This format avoid...

Implementation of the Daytime Cloud Optical and Microphysical Properties Algorithm (DCOMP) in PATMOS-x

AbstractThis paper describes the daytime cloud optical and microphysical properties (DCOMP) retrieval for the Pathfinder Atmosphere’s Extended (PATMOS-x) climate dataset. Within PATMOS-x, DCOMP is applied to observations from the Advanced Very High Resolution Radiometer and employs the standard bispectral approach to estimate cloud optical depth and particle size. The retrievals are performed within the optimal estimation framework. Atmospheric-correction and forward-model parameters, such as surface albedo and gaseous absorber amounts, are obtained from numerical weather prediction reanalysis data and other climate datasets. DCOMP is set up to run on sensors with similar channel settings and has been successfully exercised on most current meteorological imagers. This quality makes DCOMP particularly valuable for climate research. Comparisons with the Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 dataset are used to estimate the performance of DCOMP.

Evaluating and Improving Cloud Parameter Retrievals

What: A joint European/United States workshop gathered about 70 research scientists and students to review existing and new approaches to infer cloud parameters from passive and active satellite observations. The priorities of this workshop were to compare products from different teams, increase traceability of results, and discuss scientific issues common to all teams. When: 13–17 November 2011 Where: Madison, Wisconsin EVALUATING AND IMPROVING CLOUD PARAMETER RETRIEVALS

Climatology Analysis of Aerosol Effect on Marine Water Cloud from Long-Term Satellite Climate Data Records

Satellite aerosol and cloud climate data records (CDRs) have been used successfully to study the aerosol indirect effect (AIE). Data from the Advanced Very High Resolution Radiometer (AVHRR) now span more than 30 years and allow these studies to be conducted from a climatology perspective. In this paper, AVHRR data are used to study the AIE on water clouds over the global oceans. Correlation analysis between aerosol optical thickness (AOT) and cloud parameters, including cloud droplet effective radius (CDER), cloud optical depth (COD), cloud water path (CWP), and cloud cover fraction (CCF), is performed. For the first time from satellite observations, the long-term trend in AIE over the global oceans is also examined. Three regimes have been identified: (1) AOT 0.3, where CDER first increases with AOT and then levels off. AIE is easy to manifest in the CDER reduction in the second regime (named Regime 2), which is identified as the AIE sensitive/effective regime. The AIE manifested in the consistent changes of all four cloud variables (CDER, COD, CWP, and CCF) together is located only in limited areas and with evident seasonal variations. The long-term trend of CDER changes due to the AIE of AOT changes is detected and falls into three scenarios: Evident CDER decreasing (increasing) with significant AOT increasing (decreasing) and evident CDER decreasing with limited AOT increasing but AOT values fall in the AIE sensitive Regime 2.

Using the NASA EOS A-Train to Probe the Performance of the NOAA PATMOS-x Cloud Fraction CDR

An important component of the AVHRR PATMOS-x climate date record (CDR)—or any satellite cloud climatology—is the performance of its cloud detection scheme and the subsequent quality of its cloud fraction CDR. PATMOS-x employs the NOAA Enterprise Cloud Mask for this, which is based on a naive Bayesian approach. The goal of this paper is to generate analysis of the PATMOS-x cloud fraction CDR to facilitate its use in climate studies. Performance of PATMOS-x cloud detection is compared to that of the well-established MYD35 and CALIPSO products from the EOS A-Train. Results show the AVHRR PATMOS-x CDR compares well against CALIPSO with most regions showing proportional correct values of 0.90 without any spatial filtering and 0.95 when a spatial filter is applied. Values are similar for the NASA MODIS MYD35 mask. A direct comparison of PATMOS-x and MYD35 from 2003 to 2014 also shows agreement over most regions in terms of mean cloud amount, inter-annual variability, and linear trends. Regional and seasonal differences are discussed. The analysis demonstrates that PATMOS-x cloud amount uncertainty could effectively screen regions where PATMOS-x differs from MYD35.

Outcome of the third cloud retrieval evaluation workshop

Accurate measurements of global distributions of cloud parameters and their diurnal, seasonal, and interannual variations are needed to improve understanding of the role of clouds in the weather and climate system, and to monitor their time-space variations. Cloud properties retrieved from satellite observations, such as cloud vertical placement, cloud water path and cloud particle size, play an important role for such studies. In order to give climate and weather researchers more confidence in the quality of these retrievals their validity needs to be determined and their error characteristics must be quantified. The purpose of the Cloud Retrieval Evaluation Workshop (CREW), held from 15-18 Nov. 2011 in Madison, Wisconsin, USA, is to enhance knowledge on state-of-art cloud properties retrievals from passive imaging satellites, and pave the path towards optimizing these retrievals for climate monitoring as well as for the analysis of cloud parameterizations in climate and weather models. CREW also seeks t...

GEWEX cloud assessment: A review

Clouds cover about 70% of the Earths surface and play a dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere over the entire globe and across the wide range of spatial and temporal scales that comprise weather and climate variability. Satellite cloud data records now exceed more than 25 years; however, climatologies compiled from different satellite datasets can exhibit systematic biases. Questions therefore arise as to the accuracy and limitations of the various sensors. The Global Energy and Water cycle Experiment (GEWEX) Cloud Assessment, initiated in 2005 by the GEWEX Radiation Panel, provides the first coordinated intercomparison of publicly available, global cloud products (gridded, monthly statistics) retrieved from measurements of multi-spectral imagers (some with multi-angle view and polarization capabilities), IR sounders and lidar. Cloud properties under study include cloud amount, cloud height (in ter...