Anke Thoss

NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part I: Algorithm Description

Abstract New methods and software for cloud detection and classification at high and midlatitudes using Advanced Very High Resolution Radiometer (AVHRR) data are developed for use in a wide range of meteorological, climatological, land surface, and oceanic applications within the Satellite Application Facilities (SAFs) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), including the SAF for Nowcasting and Very Short Range Forecasting Applications (NWCSAF) project. The cloud mask employs smoothly varying (dynamic) thresholds that separate fully cloudy or cloud-contaminated fields of view from cloud-free conditions. Thresholds are adapted to the actual state of the atmosphere and surface and the sun–satellite viewing geometry using cloud-free radiative transfer model simulations. Both the cloud masking and the cloud-type classification are done using sequences of grouped threshold tests that employ both spectral and textural features. The cloud-type classification div...

Precipitation analysis using the Advanced Microwave Sounding Unit in support of nowcasting applications

We describe a method to remotely sense precipitation and classify its intensity over water, coasts and land surfaces. This method is intended to be used in an operational nowcasting environment. It is based on data obtained from the Advanced Microwave Sounding Unit (AMSU) onboard NOAA-15. Each observation is assigned a probability of belonging to four classes: precipitation-free, risk of precipitation, precipitation between 0.5 and 5 mm/h, and precipitation higher than 5 mm/h. Since the method is designed to work over different surface types, it relies mainly on the scattering signal of precipitation-sized ice particles received at high frequencies. For the calibration and validation of the method we use an eight-month dataset of combined weather radar and AMSU data obtained over the Baltic area. We compare results for the AMSU-B channels at 89 GHz and 150 GHz and find that the high frequency channel at 150 GHz allows for a much better discrimination of different types of precipitation than the 89 GHz channel. While precipitation-free areas, as well as heavily precipitating areas (> 5 mm/h), can be identified to high accuracy, the intermediate classes are more ambiguous. This stems from the ambiguity of the passive microwave observations as well as from the non-perfect matching of the different data sources and sub-optimal radar adjustment. In addition to a statistical assessment of the methods accuracy, we present case studies to demonstrate its capabilities to classify different types of precipitation and to work over highly structured, inhomogeneous surfaces.

NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part II: Tuning and Validation

Algorithms for cloud detection (cloud mask) and classification (cloud type) at high and midlatitudes using data from the Advanced Very High Resolution Radiometer (AVHRR) on board the current NOAA satellites and future polar Meteorological and Operational Weather Satellites (METOP) of the European Organisation for the Exploitation of Meteorological Satellites have been extensively validated over northern Europe and the adjacent seas. The algorithms have been described in detail in Part I and are based on a multispectral grouped threshold approach, making use of cloud-free radiative transfer model simulations. The thresholds applied in the algorithms have been validated and tuned using a database interactively built up over more than 1 yr of data from NOAA-12, -14, and -15 by experienced nephanalysts. The database contains almost 4000 rectangular (in the image data)-sized targets (typically with sides around 10 pixels), with satellite data collocated in time and space with atmospheric data from a short-range NWP forecast model, land cover characterization, elevation data, and a label identifying the given cloud or surface type as interpreted by the nephanalyst. For independent and objective validation, a large dataset of nearly 3 yr of collocated surface synoptic observation (Synop) reports, AVHRR data, and NWP model output over northern and central Europe have been collected. Furthermore, weather radar data were used to check the consistency of the cloud type. The cloud mask performs best over daytime sea and worst at twilight and night over land. As compared with Synop, the cloud cover is overestimated during night (except for completely overcast situations) and is underestimated at twilight. The algorithms have been compared with the more empirically based Swedish Meteorological and Hydrological Institute (SMHI) Cloud Analysis Model Using Digital AVHRR Data (SCANDIA), operationally run at SMHI since 1989, and results show that performance has improved significantly.

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

Toward Global Harmonization of Derived Cloud Products

Formerly known as the Cloud Retrieval Evaluation Workshop (CREW; see the list of acronyms used in this paper below) group (Roebeling et al. 2013, 2015), the International Cloud Working Group (ICWG) was created and endorsed during the 42nd Meeting of CGMS. The CGMS-ICWG provides a forum for space agencies to seek coherent progress in science and applications and also to act as a bridge between space agencies and the cloud remote sensing and applications community. The ICWG plans to serve as a forum to exchange and enhance knowledge on state-of-the-art cloud parameter retrievals algorithms, to stimulate support for training in the use of cloud parameters, and to encourage space agencies and the cloud remote sensing community to share knowledge. The ICWG plans to prepare recommendations to guide the direction of future research-for example, on observing severe weather events or on process studies-and to influence relevant programs of the WMO, WCRP, GCOS, and the space agencies.

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...

Inter-comparison of cloud detection and cloud top height retrievals using the CREW database

About 70% of the earth’s surface is covered with clouds. They strongly influence the radiation balance and the water cycle of the earth. Hence the detailed monitoring of cloud properties - such as cloud fraction, cloud top temperature, cloud particle size, and cloud water path – is important to understand the role of clouds in the weather and the climate system. The remote sensing with passive sensors is an essential mean for the global observation of the cloud parameters, but is nevertheless challenging. To understand the uncertainty characteristics of cloud remote sensing 12 state-of-art cloud detection and cloud top properties retrievals using SEVIRI observations were inter-compared and validated against CALIPSO and CPR measurements. Our results show that the cloud detection results of the individual algorithms are different for thin cloud layers, broken cloud fields, and aerosol situations. Cloud top height retrievals are uncertain for multilayer situations and thin cloud layers.