Rob Roebeling

The 30 year TAMSAT African Rainfall Climatology And Time series (TARCAT) data set

African societies are dependent on rainfall for agricultural and other water-dependent activities, yet rainfall is extremely variable in both space and time and reoccurring water shocks, such as drought, can have considerable social and economic impacts. To help improve our knowledge of the rainfall climate, we have constructed a 30 year (1983–2012), temporally consistent rainfall data set for Africa known as TARCAT (Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series) using archived Meteosat thermal infrared imagery, calibrated against rain gauge records collated from numerous African agencies. TARCAT has been produced at 10 day (dekad) scale at a spatial resolution of 0.0375°. An intercomparison of TARCAT from 1983 to 2010 with six long-term precipitation data sets indicates that TARCAT replicates the spatial and seasonal rainfall patterns and interannual variability well, with correlation coefficients of 0.85 and 0.70 with the Climate Research Unit and Global Precipitation Climatology Centre gridded-gauge analyses respectively in the interannual variability of the Africa-wide mean monthly rainfall. The design of the algorithm for drought monitoring leads to TARCAT underestimating the Africa-wide mean annual rainfall on average by −0.37 mm d−1 (21%) compared to other data sets. As the TARCAT rainfall estimates are historically calibrated across large climatically homogeneous regions, the data can provide users with robust estimates of climate related risk, even in regions where gauge records are inconsistent in time.

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

Inter-calibration of METEOSAT IR and WV channels using HIRS

We present a strategy to inter-calibrate the complete time series of Water Vapour (WV, 6.3 μm) and Infrared (IR, 11.8 μm) channel radiances from the MVIRI instrument on Meteosat First Generation and the SEVIRI instrument on Meteosat Second Generation. Two different inter-calibration methods are evaluated i.e., (1) inter-calibrate to a homogenised time series of HIRS observations, and (2) inter-calibrate using the so called double differencing methodology. The latter method compares, for instance, observations from two Meteosat instruments to observations of one HIRS instrument, and then calculates the difference between the two Meteosat instruments, or vice versa. Both methods are validated against reference observations, for which observations from the IASI instrument are used. In addition, the stability of the HIRS on Metop is investigated with Metop IASI observations. We have analyzed the uncertainties introduced in both methods by changes in filter functions, collocation scene variability, instrument ...

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.