Topical Groups (2014-2016)
From CrewWiki
TG - Cloud Modelling
Lead: Phil Watts (EUMETSAT)
Participants: Caroline Poulsen, Bryan Baum, ...
Link:
Summary and Scope
Cloud models for property retrievals from passive imagers are generally highly simplified compared to reality – the single layer, homogeneous and geometrically thin model has dominated for many years and has, despite the simplicity, enjoyed a great deal of success, partly because the information content in the imagers is commensurate with only a few descriptive parameters.
Nevertheless some deficiencies of the simple models are recognised. The single layer approximation is frequently shown to lead to layer average properties in multi-layer clouds. Horizontal and vertical homogeneity assumptions are, in the first case, known to lead to biases in optical thickness and, in the second case, suspected as inadequate when several particle size sensing channels are present. There are many other issues of varying complexity and impacts on retrieved cloud products.
The aims of this working group are to encourage the community to identify, characterise and provide solutions to cloud modelling issues where necessary and possible. In order to do this the working group will focus on the following aspects:
- Cloud models in use;
- Identification of outstanding cloud modelling issues and evaluation of importance;
- Solutions to the modelling issues either existing or as new research directions;
- Validating the benefits of new models;
- Need or not for consistent models across algorithms?
Note: The group’s activities cross in areas with the groups: - Use of Combined Sensors for Cloud Retrievals -, - Assessment of Retrieval Uncertainties -, and iii) Validation Sources.
TG - Cloud Detection, including detection of Arctic/Antarctic clouds
Lead: Karl-Goran Karlsson (SMHI)
Participants: Anke Thoss, Adam Dybbroe, Andi Walther, Andrew Heidinger
Link:
Summary and Scope
Cloud detection ...
TG - Use of Combined Sensors for Cloud Retrievals
Lead: Bryan Baum (University of Wisconsin–Madison)
Participants: Elisabeth Weisz, W. Paul Menzel, Andrew Heidinger
Link:
Short Description (summary and scope)
Since the advent of the NOAA polar-orbiting operational weather satellites, each platform has carried a high spatial resolution imager (AVHRR) and a lower spatial resolution sounder (HIRS). The Metop-A/B platforms carry both an AVHRR and a HIRS, and additionally a Michelson interferometer called IASI. The primary imager on the NASA Terra and Aqua platforms is MODIS, a 36-band imager, while on the Suomi-NPP platform, the imager/sounder combination is VIIRS and CrIS. Of all these platforms, the only imager that has infrared (IR) absorption channels is MODIS; the others have at most three IR window bands. The bands of interest are in the broad 4.5-µm CO2, 6.7- µm H2O, and 15-µm CO2 absorption bands. In recent work, we developed a method to construct high spatial resolution infrared (IR) absorption band radiances for an imager based on a fusion of imager and sounder data on polar-orbiting weather satellite platforms. More specifically, high spatial resolution IR absorption band radiances were constructed for VIIRS moderate-resolution bands (750 m spatial resolution at nadir) based on data from both VIIRS and CrIS (15 km spatial resolution at nadir). We note that the MODIS+AIRS combination on the NASA Aqua platform provides a unique platform to test the data fusion method since MODIS takes measurements at both the HIRS and AVHRR wavelengths of interest to cloud top property retrievals.
The goal of this topical group is to discuss the formation of a new fundamental climate data record (FCDR) in which similar IR absorption band radiances are constructed for AVHRR+IASI and AVHRR+HIRS. The benefit to merging the imager and sounder measurements is that the sounder measurements are useful for the analysis of optically thin high clouds, can help to improve cloud retrievals in polar regions, and also benefit IR phase determination, among others.
Note: The group’s activities cross in areas with the groups: - Cloud Modelling -, and - Assessment of Retrieval Uncertainties -.
TG - Microwave Cloud Remote Sensing
Lead: Ralf Bennartz (Vanderblidt Univeristy)
Participants: Anke Thoss, Adam Dybbroe, Andi Walther, Andrew Heidinger
Link:
Summary and Scope
Cloud detection ...
TG - Validation Sources
Lead: Pat Minnis (NASA)
Participants: Jed Bojanowski, ...
Link:
Short Description (summary and scope)
Validation of satellite-derived cloud properties is critical for understanding the uncertainties in the retrieved parameters and for making improvements in the retrieval algorithms. As larger and more diverse sets of independent measurements become available for assessing a given retrieval, the task of validation can sometimes appear bewildering. Varied approaches and methods for using independent reference data also can sometimes result in "validation" numbers that can mean different things to different researchers. The mission of this topical working group is to survey the available datasets and techniques for validation of each parameter to 1) make each researcher aware of the resources/methods, 2) determine the limitations of each dataset/approach, and 3) determine if there are some common datasets and approaches that can yield meaningful cross-algorithm uncertainty estimates for various satellites. The CREW-III and IV exercises provided a good start in that direction using CALIPSO and AMSR-E data to assess cloud properties. This group will seek to expand on that effort while providing individual research groups with the means to better assess their own algorithms.
TG - Assessment of level-2 Passive Imager Cloud Parameter Retrievals
Lead: Yong-SangChoi (Ewha Womans University)
Participants: Andi Walther ...
Link:
Summary and Scope
The ICWG will continue inter-comparing cloud property retrievals derived from both polar-orbiting and geostationary passive imagers. Comparisons with the CALIPSO/CALIOP and CPR/CloudSat sensors have proven to be very useful in the previous CREW workshops. These comparisons aim at identifying current capacity of retrieving the cloud parameters based on unified reference data.
The providers of cloud parameter retrievals_ are invited to provide instantaneous (level-2) cloud property retrievals, either new datasets or updates of existing datasets, for the golden days (13, 17, 18, and 22 June 2008, 3 July 2008, and 8 October 2014). These datasets will be included in the Common Database of cloud property retrievals. This database currently includes cloud property retrievals from 15 different algorithms. The providers of cloud property datasets have the option to:
- Update their algorithm descriptions;
- Update of their cloud property dataset for the golden days;
- Submit new cloud property datasets for the golden days;
The goal is to use the level-2 datasets for an inter-comparison of cloud property retrievals (and their error estimates) from both polar and geostationary passive imagers (e.g., SEVIRI observing Europe and Africa, AHI/Himawari8 observing Asia and Tropical Western Pacific, AVHRR onboard NOAA 18 and MODIS). As reference datasets, the main sources of information will be the cloud properties obtained from CLOUDSAT and/or CALIPSO observations and Cloud Liquid Water Path observations from passive microwave instruments (e.g. AMSR).
TG - Assessment of Retrieval Uncertainties
Lead: Caroline Poulsen (UK MetOffice)
Participants: Phil Watts, Nadia Smith, ...
Link:
Summary and Scope
Increasingly it has become standard to include pixel level uncertainty in the production of satellite cloud products. A number of retrieval schemes use optimal estimation to generate pixel level uncertainty through propagation of the uncertainty on the input parameters and forward model to retrieved parameters. However other techniques also provide pixel level uncertainty. Pixel level uncertainty is increasing being requested by the satellite, modelling and data assimilation community as a means of extracting the most useful information from a retrieved variable.
This working group will focus on getting uncertainty to a high level of maturity and increased acceptance and use by the community. In order to do this the working group will focus on the following aspects:
- Common definitions of uncertainty and standard naming;
- Identification of different sources of uncertainty and how to characterise them;
- Uncertainty in L3 products;
- Validation of uncertainty, identifying standard references;
- Interactions with users of uncertainty.
Note: The group’s activities cross in areas with the group: - Aggregation Methods for Climate Applications -
TG - Aggregation Methods for Climate Applications
Lead: Nadia Smith (University of Wisconsin–Madison)
Participants: Ralf Bennartz, Rob Roebeling, Robert Pincus, ...
Link:
Summary and Scope
Earth atmosphere satellite systems are typically designed to measure parameters and processes at scales suitable for weather applications, which are concerned with localized instances of atmospheric change, i.e. large variance over small space-time scales. Climate applications, on the other hand, require the detection and characterization of small variance over large space-time scales. This means that satellite measurements need to be aggregated before ingestion into climate applications. Far from being simple averaging, aggregation methods require careful study. They not only reduce data volume but they also serve to improve the signal-to-noise ratio across scale for the study of climatic change. This topical group is concerned with the study of aggregation methods for climate applications and will investigate the following two areas specifically:
- The management of systematic uncertainty, both in terms of mapping its propagation as well as in minimizing its expression at climate scales;
- The accurate representation of climate processes and parameters using weather measurements from different satellite systems.
TG - Assessment of Cloud Parameter Data Records for Climate Studies
Lead: Martin Stengel (Deutscher Wetterdienst)
Participants: ...
Link:
Summary and Scope
This Topical Group has the following mission statements:
- Focus on passive imagers , thus mainly AVHRR and MODIS cloud property records
- Potentially considered datasets: NASA MODIS coll. 6 (2000-today), Patmos-X (AVHRR, 1982-today), CLARA-A2 (AVHRR, 1982-2014), Cloud_cci (AVHRR, 1982-2014), ISCCP (AVHRR+GEOs, 1983-2009)
- Focus on following cloud variables: cloud fraction, cloud top pressure, optical thickness, thermodynamic phase
- Focus on monthly means in the beginning (later also considering monthly histograms)
- Discuss impact of L1 data quality on climate quality of the cloud records
- Investigate stability of existing datasets and their maturity in terms of trend detection
- Assess impact of orbital drift and possible methods to correct for this
Note: The group’s activities cross in areas with the group: - Aggregation Methods for Climate Applications -. Moreover the group interacts with climate group of ITSC (which focuses on TOVS/ATOVS sensors)
TG - Severe Weather Applications
Lead: Mike Pavolonis (NOAA STAR)
Participants: Bryan Baum, Anke Thoss, Adam Dybbroe, Daniel Rosenfeld, Ulrich Hamann
Link:
Summary and Scope
Severe weather applications illustrate the importance of developing sophisticated, scientific, computer algorithms to convert extremely large volumes of satellite data knowledge. The need for science based computer algorithms has never been greater as data volumes and information content will increase significantly with the next generation of operational satellites. Thunderstorms that produce large hail, damaging winds, and tornadoes are often difficult to forecast due to their rapid evolution and complex interactions with environmental features that are challenging to directly observe. Satellite data, in combination with other data sets, can be used to increase the timeliness and accuracy of severe weather warnings and the understanding of the convective life cycle. This topical group will focus on research aimed at using satellite data, perhaps in combination with other data sources, to improve severe weather forecasting and knowledge.
TG - Cloud Height for Wind Applications
Lead: Andrew Heidinger (NOAA STAR)
Participants: Phil Watts, Regis Borde
Link:
Summary and Scope
Many current Atmospheric Motion Vector (AMV) algorithms are relying on objectively derived cloud heights to assign the vertical position of their AMV tracers. In recognition of this evolution, The International Winds Working Group (IWWG) has expressed a desire to collaborate with the International Cloud Working Group. This ICWG Topical Group will focus on this collaboration. The AMV algorithms predominately use results at the edges of clouds to derive AMVs. Much of the technical discussions in this topical group will focus on the performance of the cloud height algorithms at cloud edges. Another important topic is the comparison of the methods and metrics used to report cloud height uncertainties and other diagnostic parameters for use in AMV applications.
