MESAN Mesoscale analysis of precipitation

Abstract

The Mesoscale Analysis System (MESAN) has been running operationally since April, 1997, providing science and consumers of weather information with spatially continuous fields of nine analysed meteorological parameters every hour. Data input to MESAN consists of surface observations from different observation systems, numerical weather prediction model fields, weather radar and satellite imageries, and climate information. Each data source is quality controlled before being subjected to an optimal interpolation (OI) scheme, together with data from the other sources. This paper presents MESAN's accumulated precipitation product. The methods used for interpolation of the multisource data are presented and discussed, as are the methods used to quality control each data source. Results from August-October 1995, using multisource data including gauge observations from the countries in the Baltic Sea Experiment (BALTEX) Region, exemplify the product. OI, used with a variable first guess error, has been compared with conventional inverse distance interpolation of precipitation in two catchments in mountainous terrain. Verification was conducted through modelled runoff, using areally integrated accumulated precipitation, compared with hydrograph observations. Significant improvements using OI were found in one of the catchments. The relative contribution (or importance) of each data source to the analysis has been evaluated using cross validation. Results show that gauge networks are the single most important sources and that radar imagery makes a significant contribution in areas lacking networks of dense gauges, such as the Baltic Sea. Analysis quality improves with the use of a greater number of input data sources. MESAN is an appropriate tool for creating an overall best estimate precipitation analysis and should be useful in applications where such information is required. In validating precipitation produced by numerical weather prediction models, analyses generated without the use of such model fields is recommended.